Reviewerly/exHarmony huggingface dataset:
Facial age estimation with bilateral relationships exploitation

In the environment of smart cities, human facial age estimation has become an important research topic due to its wide applications. Although a variety of methods are proposed to depict facial biologic attributes, the underlying relationships between facial appearance and its biological aging process have not been fully explored. Although relationship learning methods have been constructed in terms of modeling either the facial representations or the facial attributes, none of them model the relationships simultaneously. In this paper, we propose a unified model to explore these relationships simultaneously, coined as JREAE. In JREAE, two covariance matrices are symmetrically constructed to capture the underlying correlations from both aspects of input facial features and output age labels. In this way, the potential relationships of both feature and label are not only modeled definitely, but also explored adaptively from the training data, which is significantly different from those methods that define the relationships manually. Then, we extend the JREAE model with deep convolutional architecture (deep-JREAE) for more powerful discrimination. In addition, we present optimization algorithms to solve the proposed models with theoretical convergence and complexity proof. Finally, through extensive experiments, we not only validate the effectiveness and superior of the proposed methods in performance, but also visualize and analyze the resulting relationships.
The Technology Acceptance Model in the Collaborative Learning of Web 2.0, Web 3.0 and Web 4.0: Higher Education Case Study

Cigarette filters are one of the most littered objects in the world that damage the environment. Incorporation in construction bricks offers the prospect of limiting damage and solves the recyclability problem for a typical non-biodegradable waste. This paper aims to explore that prospect, by adding cigarette filters in the production of two samples of fired clay bricks with the cigarette filters comprising 5% and 10% of the volume. The mixing and molding processes were done manually which had an effect on the bricks when tested, The results of testing were corresponding with those of previous studies (outside Egypt), however, only one sample of 5% Cigarette butts volume bricks complied with the Egyptian standards for bricks used in non-load bearing uses. The industrial production of these bricks is highly recommended as it will surpass the required Egyptian standards, save a significant amount of natural resources and eliminate cigarette waste.
Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks

Understanding rock mineralogy is essential for formation evaluation, improving the calculation of porosity and hydrocarbon saturation. The primary method to obtain the mineralogy from a well is by applying a model to the geochemical tool's chemical elements. However, creating a mineralogical model presents challenges such as the minerals' chemical composition and the decision to include a mineral in the model. The traditional application of machine learning can make mineral models less realistic since conventional training is developed based on a set of minerals with different occurrences, lowering some minerals' representativeness. The present research proposes the stepped machine learning (SML), a stepped way to use machine learning to create a mineralogical model from chemical and mineralogical data. A database was assembled with the elemental concentration obtained with XRF analyses and the mineral concentrations obtained with XRD analyses. The chemical elements were Al, Ca, Fe, K, Mg, Mn, Na, Si, and Ti. The minerals were calcite, dolomite, quartz, clays, K-feldspar, plagioclase, and pyroxene. Four algorithms were tested: MLP, GAN, Random Forest, and XGBoost, with XGBoost showing the best results. SML was applied, where a mineral model results are used to train a subsequent model. SML allowed for a significant improvement in some models, notably to clays with an increase in R2 from 0.597 to 0.853, quartz an increase from 0.673 to 0.869, and calcite, from 0.758 to 0.862. A decrease in the mean squared error of these minerals' models was also observed. The model was applied to the geochemical logs from three wells drilled in the Brazilian pre-salt, and the results were compared with XRD analyzes. The SML model was able to honor the mineral concentrations for different rocks. It is demonstrated that the integration between machine learning tools and geological knowledge in SML was crucial for creating a representative mineralogical model.
Application of Expert System for Diagnosing Gastric Disease Android Based with Certainty Factor Method

Expert systems are computer programs that mimic reasoning an expert with expertise in an area of knowledge, the present study, how to diagnose diseases of the stomach to build an expert system that can analyze the symptoms of the disease into a decision, the method Cerataity Factor as a method for calculating confidence in the value provided by the patient's symptoms. The purpose of this study is to provide convenience to the public for mediagnosa a disease of the stomach suffered and gave information about diseases of the stomach and provide solutions when terjankit gastric diseases. Of the many diseases in carefully then there are various symptoms sample to meet the needs of the weights that have been defined by experts.
The Design Model for Robotic Waitress

Abstract With the rapid development of traditional industries, intelligent robots have been widely used in the hospitality industry. Although the development of intelligent robots faces a positive trend and a good market in the hospitality industry, it also faces the problem that robots cannot effectively collect and use user data in the field of human–computer interaction. It not only affects the interaction experience between users and robots, but also prevents companies from getting valuable feedback in a timely manner. In order for intelligent robots to effectively utilize interactive information, the user experience of robot entertainment is improved. This paper proposes and establishes a basic technical model called iRCXM. Combining the iRCXM model with a decision tree classification algorithm is excepted effectively improve the interaction experience between humans and robots in hospitality. This paper designs a model of intelligent robot based on decision tree algorithm. The model divides the user into three sections, each corresponding to a different standard function. Using a decision tree classification algorithm model is excepted effectively judge users’ current stage and whether they can move to the next stage. When the user reaches the final stage, it proves that the user has obtained a good interactive experience. At the same time, for users at different stages, the model will provide strategies for downward transformation so that companies can adjust and improve existing problems in a timely manner. In addition, the research developed a robot user interaction system based on the existing technology. The system is based on Android. Using HTTP protocol and Baidu Cloud AI API to realize simple face recognition and Sanbot-OpenSDK to implement simple robot control, the development of this system is to verify the feasibility of the model. The developed samples were tested in a real environment and feedback from customer experience was collected through semi-structured interviews. Finally, the feasibility of the model is verified.
Deep supervised hashing using quadratic spherical mutual information for efficient image retrieval

Several deep supervised hashing techniques have been proposed to allow for extracting compact and efficient neural network representations for various tasks. However, many deep supervised hashing techniques ignore several information-theoretic aspects of the process of information retrieval, often leading to sub-optimal results. In this paper, we propose an efficient deep supervised hashing algorithm that optimizes the learned compact codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of efficient image hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI). Apart from demonstrating the effectiveness of the proposed method under different scenarios and outperforming existing state-of-the-art image hashing techniques, this paper provides a structured way to model the process of information retrieval and develop novel methods adapted to the needs of different applications.
Selfish Sparse RNN Training

Sparse neural networks have been widely applied to reduce the computational demands of training and deploying over-parameterized deep neural networks. For inference acceleration, methods that discover a sparse network from a pre-trained dense network (dense-to-sparse training) work effectively. Recently, dynamic sparse training (DST) has been proposed to train sparse neural networks without pre-training a dense model (sparse-to-sparse training), so that the training process can also be accelerated. However, previous sparse-to-sparse methods mainly focus on Multilayer Perceptron Networks (MLPs) and Convolutional Neural Networks (CNNs), failing to match the performance of dense-to-sparse methods in the Recurrent Neural Networks (RNNs) setting. In this paper, we propose an approach to train intrinsically sparse RNNs with a fixed parameter count in one single run, without compromising performance. During training, we allow RNN layers to have a non-uniform redistribution across cell gates for better regularization. Further, we propose SNT-ASGD, a novel variant of the averaged stochastic gradient optimizer, which significantly improves the performance of all sparse training methods for RNNs. Using these strategies, we achieve state-of-the-art sparse training results, better than the dense-to-sparse methods, with various types of RNNs on Penn TreeBank and Wikitext-2 datasets. Our codes are available at https://github.com/Shiweiliuiiiiiii/Selfish-RNN.
Detection of bidirectional system-environment information exchanges

Quantum memory effects can be related to a bidirectional exchange of information between an open system and its environment, which in turn modifies the state and dynamical behavior of the last one. Nevertheless, non-Markovianity can also be induced by environments whose dynamics is not affected during the system evolution, implying the absence of any physical information exchange. An unsolved open problem in the formulation of quantum memory measures is the apparent impossibility of discerning between both paradigmatic cases. Here we present an operational scheme that, based on the outcomes of successive measurements processes performed over the system of interest, allows one to distinguish between both kinds of memory effects. The method accurately detects bidirectional information flows in diverse dissipative and dephasing non-Markovian open system dynamics.
Evaluating Machine Learning Approaches for Discovering Optimal Sets of Projection Operators for Quantum State Tomography of Qubit Systems

Abstract Finding optimal measurement schemes in quantum state tomography is a fundamental problem in quantum computation. It is known that for non-degenerate operators the optimal measurement scheme is based on mutually unbiassed bases. This paper is a follow up from our previous work, where we use standard numerical approaches to look for optimal measurement schemes, where the measurement operators are projections on individual pure quantum states. In this paper we demonstrate the usefulness of several machine learning techniques – reinforcement learning and parallel machine learning approaches, to discover measurement schemes, which are significantly better than the ones discovered by standard numerical methods in our previous work. The high-performing quorums of projection operators we have discovered have complex structure and symmetries, which may imply that the optimal solution will possess such symmetries.
A High-Frequency Analysis of Bitcoin Markets

We study trading of Bitcoin (BTC) against US dollar (USD) on exchanges on three continents, Bitfinex, Bitstamp and Coinbase Pro. We use a high frequency dataset that contains transactions and order book information. The BTCUSD market is highly liquid in terms of bid-ask spreads and order book depth. While spreads are low, markets are not integrated. Persistent differences exist between the three exchanges in terms of transaction and posted prices, and markets are crossed most of the time. The liquidity of the Bitcoin exchanges is predominantly determined by local factors and is essentially independent of liquidity in equity and FX markets.
Sampling Moiré method using non-sinusoidal grating

An important task in the sampling Moiré method is phase measurement of a grating image by using the phase of the Moiré fringe from a single-shot image. In order to reducing the phase errors that are due to non-sinusoidal functions in any phase measurement systems that use phase-shifting techniques one should take in to account other harmonics of the grating function. The proposed formulation in this work removes this kind of errors effectively. At first it is shown that by down-sampling of a periodic function the gained Moiré function is a stretched version of the original one. Then, an equation is obtained for the Moiré phase function based on this result. A numerical solution method is presented to solve the equation with a sufficient accuracy. The simulation results verify the effectiveness of the proposed method.
ATHENA: Contributing to Development of Higher Education Institutions for the Digital Age

Facilitated by public administrations and the European Union, higher education institutions should support their teachers so they develop the skills for online and other forms of teaching and learning opened up by the digital era and should exploit the opportunities presented by technology to improve the quality of teaching and learning. The article focuses on new European Union grant programs that empower the increasing of digital literacy in the higher education area, developing cooperation, and overcoming challenges during the coronavirus pandemic. This initiative can empower a new European University and support an international project aimed at certification of professional educators with the participation of a Russian partner.
Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective

Knowledge distillation is an effective approach to leverage a well-trained network or an ensemble of them, named as the teacher, to guide the training of a student network. The outputs from the teacher network are used as soft labels for supervising the training of a new network. Recent studies \citep{muller2019does,yuan2020revisiting} revealed an intriguing property of the soft labels that making labels soft serves as a good regularization to the student network. From the perspective of statistical learning, regularization aims to reduce the variance, however how bias and variance change is not clear for training with soft labels. In this paper, we investigate the bias-variance tradeoff brought by distillation with soft labels. Specifically, we observe that during training the bias-variance tradeoff varies sample-wisely. Further, under the same distillation temperature setting, we observe that the distillation performance is negatively associated with the number of some specific samples, which are named as regularization samples since these samples lead to bias increasing and variance decreasing. Nevertheless, we empirically find that completely filtering out regularization samples also deteriorates distillation performance. Our discoveries inspired us to propose the novel weighted soft labels to help the network adaptively handle the sample-wise bias-variance tradeoff. Experiments on standard evaluation benchmarks validate the effectiveness of our method. Our code is available at \url{https://github.com/bellymonster/Weighted-Soft-Label-Distillation}.
Key principles of developing research library’s website

Key services provided through the websites of scientific and technical libraries are discussed. Various structures of libraries’ websites or Internet-portals are described, and their feasibility is substantiated. Conclusions are made on the need for continuous website promotion in search engines in order to attract new users. Promotion of research studies to the library’s patron organizations can be accomplished through information awareness services like electronic exhibitions, news services, etc. A version of researcher’s personal account and account-based services are described. The personal accounts give researchers access to the full range of services of sci-tech libraries and provide the feedback option. Methodological and consulting services are characterized. The new version of the website of the Library of Pushchino Research Center that features online access to all available library resources. The author concludes on the need for further improvement of relevant information user services.
Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising

Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper proposes a new image denoising algorithm based on low rank matrix restoration in order to solve this problem. The proposed algorithm introduces the non-local self-similarity error between the clear image and noisy image into the weighted Schatten p-norm minimization model using the non-local self-similarity of the image. In addition, the low rank error is constrained by using Schatten p-norm to obtain a better low rank matrix in order to improve the performance of the image denoising algorithm. The results demonstrate that, on the classic data set, when comparing with block matching 3D filtering (BM3D), weighted nuclear norm minimization (WNNM), weighted Schatten p-norm minimization (WSNM), and FFDNet, the proposed algorithm achieves a higher peak signal-to-noise ratio, better denoising effect, and visual effects with improved robustness and generalization.
GEML: A grammar-based evolutionary machine learning approach for design-pattern detection

Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided.
Parallel Algorithms for Minimal Nondeterministic Finite Automata Inference

The goal of this paper is to develop the parallel algorithms that, on input of a learning sample, identify a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA that represents the target regular language is sought. We define the task of finding an NFA, which accepts all positive examples and rejects all negative ones, as a constraint satisfaction problem, and then propose the parallel algorithms to solve the problem. The results of comprehensive computational experiments on the variety of inference tasks are reported. The question of minimizing an NFA consistent with a learning sample is computationally hard.
Generative Zero-shot Network Quantization

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, \textit{e.g.,} due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.
Gender differences in Learning Arabic Language Proficiency via M-learning among Malaysia University Students

The integration of multimedia would enable teachers to take maximum advantage of technology to educate non-native students regarding Arabic language proficiency. Multimedia teaching has many benefits compared to the conventional teaching model, where we differentiate by incorporating multimedia which makes Arabic classes more energetic, vibrant and interesting. However, the significant impact of multimedia learning could be different toward gender as their interest and preference could be differ. Therefore, this study aims to investigate gender differences in learning Arabic language proficiency via M-learning among Malaysia university students. This study is a pre-test and post-test quasi experimental study which comprises of 84 males and 189 females in treatment group; and 197 students in control groups. All of the participants are lower beginner Arabic language learners from four universities in peninsular Malaysia which were selected through stratified random sampling technique. The findings revealed that male learners received substantially greater adjusted average scores on the performance test versus females using the same courses. Noticeable correlations between M-learning modes and gender on achievement have been established. In regards, the concepts of M-Learning must be taken into account when developing online learning syllabuses in order to facilitate Arabic language learning. The findings of this study will help lecturers to improve their teaching practices in this era of technology advancement and provide a consideration on gender differences of the preference towards mobile learning practice.