Reviewerly/exHarmony huggingface dataset:
Stacking learning with coalesced cost filtering for accurate stereo matching

Deep learning based stereo matching algorithms have produced impressive disparity estimation for recent years; and the success of them has once overshadowed the conventional ones. In this paper, we intend to reverse this inferiority, by leveraging Stacking Learning with Coalesced Cost Filtering to make the conventional algorithms achieve or even surpass the results of deep learning ones. Four classical and Discriminative Dictionary Learning (DDL) algorithms are adopted as base-models for Stacking. For the former ones, four classical stereo matching algorithms are employed and regarded as 'Coalesced Cost Filtering Module'; for the latter supervised learning one, we utilize the Discriminative Dictionary Learning (DDL) stereo matching algorithm. Then three categories of features are extracted from the predictions of base-models to train the meta-model. For the meta-model (final classifier) of Stacking, the Random Forest (RF) classifier is selected. In addition, we also employ an advanced one-view disparity refinement strategy to compute the final refined results more efficiently. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed algorithm outperforms other four most challenging stereo matching algorithms. Besides, the submitted online results even show better results than deep learning ones.
A Dual Approach to Graph CNNs

Convolutional neural networks (CNNs) have been very successful with learning on grid-based data such as time series and images. However, traditional CNNs do not perform well on irregular-structured data defined on a graph. Graph convolutional neural networks (graph CNNs) define convolutional layers using graph signal processing (GSP) concepts. They perform well on data defined on a graph such as citation networks and NYC taxi pickup data. Polynomial filter graph CNNs use a polynomial of the graph adjacency matrix A in the convolutional layer. However, they have been shown to fail on graph classification problems when the convolutional layer produces the same output for non-isomorphic graphs and identical graph signals. Recently, a spectral domain shift, M, has been proposed in GSP as the dual to the vertex domain shift and adjacency matrix, A. This has led to a spectral domain theory, dual to the existing vertex domain theory. In this paper, we redefine and dualize the convolutional layer for graph CNNs using M in the spectral domain. In doing so, we solve the problem of graph CNNs failing when applied to certain graph inputs. We show theoretically and experimentally the advantages of our solution on both synthetic and real datasets.
Meta-Learning Causal Feature Selection for Stable Prediction

Conventional predictive models in machine learning are based on I.I.D. hypothesis between training and testing data. However, such a hypothesis is fragile in the real world, and the model minimizing empirical errors on training data does not perform well on testing data, which makes the prediction unstable. This instability can be found widely in domain generalization, active learning, and transfer learning, etc. In this paper, we propose a novel Meta-learning Causal Feature Selection (MCFS) model for general Non-I.I.D. image classification. In MCFS, we jointly optimize a convolutional network and a causal parameter for identifying causal variables on meta-training and meta-testing data which simulate the distribution shifts in Non-I.I.D. problems. Extensive experiments conducted on public VLCS and NICO datasets demonstrate the effectiveness of the proposed MCFS, which outperforms the state-of-the-art methods.
Attentiveness Monitoring and user Record Maintenance in Virtual Classrooms

Virtual classrooms rely on video conferencing tools like Google Meet, Microsoft Teams or Zoom, to carry out lecture sessions. Although previously it was an additional tool, the pandemic scenario made it the primary mode of taking classes. It is observed that lecturers find it hard to monitor and look after every student in such a scenario due to divided webcam feeds, simultaneous presentation and replying on chats. An automated monitoring system can aid in such a scenario where the user's local system can be used for localized calculation of attentiveness using face and eye detection. A simulated back-end architecture is developed to propose a proof-of-concept for the same using a path compressed master-Trie structure.
Research on seismic wave first arrival Picking Based on improved U-Net

Abstract For the current problem of low accuracy of seismic wave phase pickup, this paper proposes a method based on the improved U-Net seismic wave P-wave and S-wave first-to-arrival pickup. Firstly, in order to reduce the impact of pre-processing operations such as filtering, the 20,000 three-component seismic waveform data will be normalized as input. Secondly, in order to reduce the loss of information during the seismic waveform extraction and increase the amount of information of effective features at the starting point, the Residual Unit is added to the encoder part of U-Net only, instead of adding it to the decoder part. In order to reduce the loss of high frequency information of seismic waveform, convolution operation is used instead of pooling operation. Finally, the probability distributions of P-wave, S-wave and noise are used as the output. The results show that the accuracy of the first-to-arrival pickup of P-wave is 93% and the accuracy of the first-to-arrival pickup of S-wave is 90% of the test set. The improved U-Net increased 30.9% and 6.2% in P-wave accuracy, 7.6% and 2.8% in S-wave accuracy, 39.6% and 4.0% in the P-wave recall, and 41.5% and 4.7% in the S-wave recall, respectively, compared with the conventional STA/LTA and U-Net. The improved U-Net outperforms the other two models in both evaluation metrics of recall and accuracy. The research results of this paper are of great practical significance in the localization of earthquakes, the interpretation of the earthquake gestation mechanism, and the prevention and mitigation of earthquakes.
Efficient Techniques For Human Face Occlusions Detection and Extraction

Nowadays, Facial occlusion is an interesting problem. It strongly affects the efficiency of the face recognition systems. This work is based on human face analysis to extract three types of occlusions using artificial intelligence techniques. Indeed, we are interested in beard and mustache, also eyeglasses detection, and extraction. We begin with the extraction of the face of the person. Using Viola-Jones algorithm based on Vision Cascade Detector. Then, the extraction of the beard and the mustache are used using two performance methods; the first using the Fuzzy C-means method, the second is using filters and morphological operations. After that, we detect the presence of the eyeglasses. The methods applied to show honorable results.
A Survey of Sentiment Analysis of Internet Textual Data and Application to Pakistani YouTube User Comments

With the advent of social media, e-commerce and video sharing websites, a large amount of textual data depicting people's opinions is being generated. This text has a great impact on other web users, product sellers and content creators. Text mining and emotion detection using text analysis has prospered a lot in recent years. Sentiment Analysis is the method of mining emotions, reviews and opinions of web users. In this paper, a survey of text Sentiment Analysis techniques is presented. Sentiment Analysis is applied to comments extracted from Pakistani news videos. Different machine learning algorithms previously surveyed in this paper have been applied to this dataset. Our results have shown that transfer learning can be effectively used for Sentiment Analysis.
In-Between Layers Modular Residual Neural Network for the Classification of Images

Convolutional neural networks encounter exploding/vanishing gradient and higher training error which cannot be overcome by adding layers, enhancing initialization methods, and better optimizers. Residual Neural Networks (ResNet) include added layers into residual blocks. This new design focuses mainly on activation inside the residual blocks and is more successful than CNN in object classification, detection, and segmentation. Deep residual networks have an issue of diminishing feature reuse related to the flowing through the network gradient, but not through all residual blocks. Thus, some blocks might be skipped or make small contributions to the final goal. Three simple ways to reduce the depth and increase the representation power of residual blocks, are adding convolutional layers per block, widening the convolutional layers by adding feature planes, and increasing filter sizes in convolutional layers. Here we propose In-Between Layers Modular (IBLM) ResNet - a modification of Wide Residual Net. The proposed model widens the convolutional layers by adding feature planes interpreted as increase of filter numbers for each convolution layer of our residual module, and adding some topology changes. We demonstrate the IBLM ResNet classification performance on imbalanced datasets with various optimizers and additionally improve the testing results using ensemble methods.
Ontology-Based Extraction of Kazakh Language Word Combinations in Natural Language Processing

This article provides an ontological model of nominative word combinations in the Kazakh language. It is necessary for creation of the automated templates for search of nominative word combinations of the Kazakh language in text corpora. The presented model expands the theory of applied linguistics in the field of extracting information from the text during corpus studies. The results will be used in semantic searches, Q&A systems and in the development of software applications for obtaining knowledge, as well as for training and evaluation of knowledge on the syntax of the Kazakh language in the system of e-learning.
Novel Architecture for Lifting Discrete Wavelet Packet Transform With Arbitrary Tree Structure

This brief presents a novel pipelined VLSI architecture for computing discrete wavelet packet transform (DWPT) with an arbitrary wavelet tree. Coefficients for different levels are computed in a series of stages. Each stage consists of a bypassed wavelet filter and circuit for reordering intermediate coefficients. The proposed lifting-based wavelet filter computes high- and low-pass coefficients in series. In order to accommodate the arbitrary tree structure, the filter either computes the coefficients or bypass the samples. The reordering of intermediate coefficients forms a subband required for next-level computation. The coefficients are computed in a serial manner and reordering of intermediate coefficients reduce not only the memory elements but also the circuit complexity. The proposed pipelined architecture reduces the requirement of memory elements by 50%. Furthermore, the hardware implementation results show that the area and power requirement are reduced by 33% and 20%, respectively.
Residential Photovoltaic Power Forecasting Considering Division of Weather Type Index Interval

Residential photovoltaic (PV) system's day-ahead power forecasting provides a basis for smart home energy management (HEM). However, the improvement of forecasting accuracy is limited by the small installed capacity and the difficulty in obtaining precise solar irradiance data. Therefore, a PV power forecasting method based on weather classification is proposed, considering the division of weather type index intervals (DWTII). Firstly, the weather type index (WTI) is defined according to hourly weather type forecasting data which is readily available to home users. By analyzing the relationship between weather types and solar irradiance, the WTI can be acquired as an acceptable input to NN. Then, the data set is partitioned by dividing the weather type index intervals, so that the forecasting models for different day types are established based on back propagation neural network (BP-NN). The example results show that the proposed prediction model can effectively identify the abrupt fluctuation characteristics of PV power in extreme weathers. Thus, the accuracy of the day-ahead PV power forecasting in residential PV systems can be improved.
Measuring the Influence of Methods to Raise the E-Awareness of Cybersecurity for Medina Region Employees

The use of computers has become inevitable for businesses, especially in the last decades. Whether in private or in public sectors, people have become extremely attached to the use of smart digital devices, such as smartphones, laptops, tablets, etc. However, the digitisation has made life easier and more comfortable, but this huge change in life has brought with it several challenges and raised the average of criminal activities known as cybercrimes. The observations showed that the main reason behind most people facing these crimes is the lack of awareness of cybersecurity and cybercrimes. This research sheds lights on cybersecurity and cybercrimes, measures the government’s e-awareness of cybersecurity, and identifies the types of most effective material in raising the e-awareness of cybersecurity and cybercrimes. The study has divided the data into different categories, in consistent with (age, gender, education, and ethic). The sample size was 243 staff. The age of the staff was divided into three different groups, in relation to their IT capabilities. The suggestion was that the primary age group target was between (20 and 35) years and then between (36 and 50) as well as (51 and 60) years. Every participant within the sample was exposed to cybersecurity e-awareness by using different tools. The study indicates that directing sources to provide Arabic content through videos or audio clips, they are more effective than other methods, thus contributing to saving resources from the loss in textual awareness. Also, there is poor e-awareness among employees form an Arab origin, while the non-Arab have a much better awareness. The most effect cybersecurity measure is to enhance the e-awareness, which affects the employees based on the employee’s level.
Development and effectiveness of mobile learning teaching materials to increase students’ creative thinking skills

Abstract This study aims to develop mobile learning (m-learning) teaching materials on straight-line equations and analyze its effectiveness in increasing junior high school students’ creative thinking skills. The method used is the R&amp;D developed by Alessi and Trollip. The stages of this research include planning, design, and development. The planning stage includes determining the scope, identifying existing teaching materials and student characteristics, and collecting references. The design stage includes analysis of the material concept, making flowcharts and storyboards of teaching materials. The development stage includes preparing teaching materials through expert validation and two trials, making mobile learning applications, and evaluating teaching materials to analyze their effectiveness. The teaching materials were evaluated by material, and media experts then tested on 30 students. The m-learning teaching material developed is an Android smartphone application that students can use as a support source for learning mathematics. This study’s subjects were all class VIII Yastrib Junior High School students, as many as 30 people. The results showed that the m-learning teaching materials on straight line equations developed were feasible to use. Their effectiveness in developing creative thinking skills in the learning process was considered adequate for students with an effect size value of 2.51, which was in the very high category.
A Fast Group Sparsity Based Phase Retrieval Algorithm for Non-Coherent DOA Estimation

A non-coherent DOA estimation algorithm for multiple snapshots is presented. The non-coherent DOA estimation problem is firstly formulated as a non-convex joint group sparsity based phase retrieval problem, and then replaced by its convex surrogate alternative through applying the majorization-minimization technique, and finally the proximal gradient method is employed to solve the surrogate problem. The proposed algorithm is referred to as fasT grOup sparsitY Based phAse Retrieval (ToyBar). Compared to the state of the art, the proposed one achieves a better performance with a lower computational complexity, as demonstrated by computer simulations.
Ensuring Security of Encrypted Information by Hybrid AES and RSA Algorithm with Third-Party Confirmation

Information transmission security and wellbeing is an intriguing issue right now. secure information transmission alludes to the exchange of information, for example, private or exclusive data over a safe channel. Many secure transmission techniques require any encryption. As of late system, security has become a significant issue. Encryption has come up as an answer and assumes a significant job in the data security framework. Numerous procedures are expected to secure mutual information. Different encryption calculations are utilized for the security and wellbeing of information transmission. The principle objective is to manage the structure of any encryption calculation against unapproved assaults. Outsider check is utilized to expand the security of every single pragmatic application and execution. This paper introduces estimation and ensuring security of encrypted information by hybrid AES and RSA algorithm with third-party confirmation. This is a secured authentication system that supports both the privacy of messages and the authenticity of communicating parties is proposed. The key goal guiding the encryption algorithm's design must be a defense against unauthorized attacks. This paper proposes an encryption algorithm to address security and privacy to protect the data from unauthorized persons.
Discriminative and Geometrically Robust Zero-Watermarking Scheme for Protecting DIBR 3D Videos

Copyright protection of depth image-based rendering (DIBR) 3D videos is crucial due to the popularity of these videos. Despite the success of recent watermarking schemes, it is still challenging to ensure the robustness against strong geometric attacks when both lossless quality and distinguishability of protected videos are required. In this paper, we pro-pose a novel zero-watermarking scheme to improve the performance under strong geometric attacks when satisfying the other two requirements. In our scheme, CT-SVD-based features are extracted to ensure both distinguishability and robustness against signal processing and DIBR conversion at-tacks, while a SIFT-based rectication mechanism is designed to resist geometric attacks. Further, an attention-based fusion strategy is proposed to complement the robustness of rectied and unrectied CT-SVD features. Experimental results demonstrate that our scheme outperforms the existing zero-watermarking schemes in terms of distinguishability and robustness against strong geometric attacks such as rotation, cyclic translation and shearing.
An empirical comparison of validation methods for software prediction models

Abstract Model validation methods (e.g., k‐ fold cross‐validation) use historical data to predict how well an estimation technique (e.g., random forest) performs on the current (or future) data. Studies in the contexts of software development effort estimation (SDEE) and software fault prediction (SFP) have used and investigated different model validation methods. However, no conclusive indications to suggest which model validation method has a major impact on the prediction accuracy and stability of estimation techniques. Some studies have investigated model validation methods specific to data about either SDEE or SFP. To the best of our knowledge, there is no study in the literature, which has employed different validation methods both with SDEE and SFP data. The aim of this paper is to consider different methods (10) from the family of cross‐validation (CV) and bootstrap validation methods to identify which one contributes to obtaining a better prediction accuracy for both types of data. We also evaluate which model validation methods allow the estimation techniques to provide stable performances (i.e., with lower variance). To this aim, we present an empirical study involving six datasets from the domain of SDEE and six datasets from the SFP domain. The results reveal that repeated 10‐fold CV with SDEE and optimistic boot with SFP data are the model validation methods that provide a better prediction accuracy in a greater number of experiments than the other model validation methods. Furthermore, a model validation method can improve the prediction accuracy up to 60% with SDEE data and up to 36% when employing SFP data. The analysis also reveals that repeated fivefold CV produces more stable performances when the experiments are repeated on the same data.
Social Recommendation Combining Implicit Information and Rating Bias

In recent years, more and more recommendation algorithm considers social information. However, in the existing social recommendation algorithm focused on the rating prediction task, the user&#x0027;s rating bias and the item&#x0027;s rating bias are often not considered. Besides, the existing social recommendation algorithms usually fail to deal with the sparse problem of social information. Concerning the problem of ignores rating bias and social information sparsity in the social recommendation, this paper proposes a novel recommendation model that combines implicit information and rating bias based on the traditional matrix factorization model. The social information used includes explicit social information and implicit social information generated by collaborative user network embedding methods. Experimental results on two real public datasets show that compared with other recommendation models, the proposed model reduces 1.13% to 10.09% and 3.14% to 8.38% respectively in the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). It shows that our proposed recommendation algorithm has a better recommendation effect.
Qualitative Assessment of an Industrial Business Entity: Economic Potential and Maximum Performance

Currently, the main factor and a necessary condition for the stable position of the enterprise is the assessment of its economic potential. The paper analyzes the etymology of the definition of the “economic potential of the enterprise.” It was found that, despite the constant attention of economists to the problem, there is no consensus on the definition of “economic potential” and the methodology for calculating it. The authors proposed a phased methodological toolkit and a system of indicators, the use of which allows determining the level of the economic potential of an economic entity. On the example of a specific industrial enterprise, a detailed calculation and assessment of its economic potential are made. It is confirmed that an industrial complex enterprise may have high economic potential, but not use it effectively enough in practice.
User-Preference Based Knowledge Graph Feature and Structure Learning for Recommendation

Introducing knowledge graphs as side information into recommendation can effectively mitigate data sparsity and cold start problems. However, the existing methods cannot simultaneously acquire the feature information and structure information of the knowledge graph. In this paper, we propose RKG, a multi-task semantic feature and high-order structure learning approach for knowledge graph assisting recommendation. RKG, which consists of a Recommender module, a Knowledge graph feature learning module and a knowledge Graph structure learning module, is a deep end-to-end framework that utilizes knowledge graph’s latent information to enhance the recommender’s performance. Information is shared among the three modules through cross unit and exchange unit to interact automatically with multiple learning tasks. Extensive experiments on three public datasets show that RKG has achieved superior and stable performance in top-K recommendation, click-through rate prediction and sparse user-item interaction scenarios over several state-of-the-art baselines.