Reviewerly/exHarmony huggingface dataset:
Artificial intelligence, opportunities and limitations of its use in education

The article considers the problem of using artificial intelligence (AI) technologies in the educational process. Based on a review of foreign scientific publications, the author defines the essence of artificial intelligence, the opportunities that it provides to both teachers and students in the framework of educational practice. It is noted that competencies in the field of artificial intelligence have become one of the important technological skills of the twenty–first century. Having competencies in the field of artificial intelligence, people can critically evaluate digital technologies in general, communicate and collaborate effectively with their help, as well as use them as a tool in the workplace and at home. The author presents and analyzes the European structure of digital competence of teachers based on the DigCompEdu platform. It is concluded that artificial intelligence can facilitate the work of teachers, and students can creatively solve educational problems. At the same time, teachers need to improve their pedagogical and technological competence in order to create appropriate learning conditions that make it possible to maximize the benefits that artificial intelligence provides.
Multiclass Sentiment Analysis of Twitter Data Using Machine Learning Approach

Given the exponential rise in the use of social media, researchers are attracted to sentiment analysis as a domain in natural language processing (NLP). Most of the previous work was concentrated on text classification as binary or ternary, but the problems in the real-world are more complicated than that. This work uses machine learning methods to do sentiment analysis on Twitter data for multiclass classification along with N-gram (unigram, bigram, and trigram) feature extraction. Data is categorized into neutral, high positive, moderate positive, moderate negative, and high negative. For actual implementation, publicly available “twitter_samples” dataset by NLTK corpora is used. The experiments are first conducted with a support vector regressor, multinomial logistic regression (SoftMax), decision tree regressor, and random forest regress or for unigrams only. Afterward best performing model decision tree is further used along with N-gram features for result analysis. The outcome gives better accuracy of 99% with bigram and trigram features. By the proposed system, sentiment in a text can be sensed in terms of context rather than just the meaning of words.
Recent Advances and Benchmarking of NewSQL for OLTP and OLAP in the Big Data Age

While the never-ending debate between relational and NoSQL database is ongoing, a new competitor, NewSQL, has quietly entered the field. Introduced in 2011, NewSQL is not as popular as relational or NoSQL database. The main selling point of NewSQL is its ability to scale horizontally while preserving ACID properties and thus preserving the support to handle OLTP workloads. Given the active research and evolving developments of NewSQL in the last decade, this research paper aims to identify how far has NewSQL advanced to and how comparable is it to existing database systems in terms of On-Line Transaction Processing (OLTP) and On-Line Analytics Processing (OLAP) to accommodate big data. Three research questions have been formulated as part of the systemic literature review (SLR) followed by an experimental benchmarking to validate the results from the SLR. The results show that while NewSQL still has room to improve, it is definitely ready to be used in productions, albeit having certain obstacles which may need to be addressed such as expertise in deployment and maintenance, as well as performance. One limitation of this research is that the testing was conducted on a single node and future research could include performance testing on multiple nodes.
Designing Evolving Cyber-Physical-Social Systems: Computational Research Opportunities

Abstract Cyber-physical-social systems (CPSS) are natural extensions of cyber-physical systems that add the consideration of human interactions and cooperation with cyber systems and physical systems. CPSS are becoming increasingly important as we face challenges such as regulating our impact on the environment, eradicating disease, transitioning to digital and sustainable manufacturing, and improving healthcare. Human stakeholders in these systems are integral to the effectiveness of these systems. One of the key features of CPSS is that the form, structure, and interactions constantly evolve to meet changes in the environment. Designing evolving CPSS includes making tradeoffs amongst the cyber, the physical, and the social systems. Advances in computing and information science have given us opportunities to ask difficult and important questions, especially those related to cyber-physical-social systems. In this paper, we identify research opportunities worth investigating. We start with theoretical and mathematical frameworks for identifying and framing the problem—specifically, problem identification and formulation, data management, CPSS modeling, and CPSS in action. Then we discuss issues related to the design of CPSS including decision-making, computational platform support, and verification and validation. Building on this foundation, we suggest a way forward.
EfficientNetV2-based dynamic gesture recognition using transformed scalogram from triaxial acceleration signal

Abstract In this paper, a dynamic gesture recognition system is proposed using triaxial acceleration signal and image-based deep neural network. With our dexterous glove device, 1D acceleration signal can be measured from each finger and decomposed to time-divided frequency components via wavelet transformation, which is known as scalogram as image-like format. To feed-forward the scalogram with single 2D, convolutional neural networks allows the gesture having temporality to be easily recognized without any complex system such as RNN, LSTM, or spatio-temporal feature as 3D CNN, etc. To classify the image with general input dimension of image RGB channels, we numerically reconstruct fifteen scalograms into one RGB image with various representation methods. In experiments, we employ the off-the-shelf model, EfficientNetV2 small-to-large model as an image classification model with fine-tuning. To evaluate our system, we bulid our custom bicycle hand signals as dynamic gesture dataset under our transformation system, and then qualitatively compare the reconstruction method with matrix representation methods. In addition, we use other signal transformation tools such as the fast Fourier transform and short-time Fourier transform and then explain the advantages of scalogram classification in the terms of time-frequency resolution trade-off issue.
Identification of pegmatites zones in Muiane and Naipa (Mozambique) from Sentinel-2 images, using band combinations, band ratios, PCA and supervised classification

Remote sensing has been widely used in Geological Sciences for different applications, such as to identify geological and mineralogical objects and surface alteration changes. This study aimed to analyze the Sentinel-2 potential to detect pegmatite bodies and associated alteration zones in Muiane and Naipa in Mozambique. Different remote sensing techniques were applied to a Sentinel-2 image: RGB combinations, band ratios, principal component analysis (PCA), and supervised image classification algorithms such as the Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). MLC was used as a benchmark classifier to evaluate the performance of SVM because MLC is the predominant algorithm employed in remote sensing classification studies. For that, several statistical metrics based on the confusion matrices were computed, namely accuracy, Kappa index, precision, recall, and f-score, among others. This study allows identifying the location of pegmatites by direct identification and segregating between hydrothermally altered zones and non-altered areas through remote sensing data/techniques, supported by field data. The field campaigns allowed for validating the results obtained and verifying the pegmatites identified using Sentinel-2 data that were not previously mapped. Moreover, reflectance spectroscopy studies in the laboratory were conducted on the samples collected in the field campaigns allow to validate the adequacy of the methodology proposed in this study. The results show that the precise identification of pegmatite targets requires a high spatial resolution such as Sentinel-2 images. Thus, with the integration of high spatial and spectral resolution data, a potential level of precision and accuracy can be achieved in the study areas.
VAESim: A probabilistic approach for self-supervised prototype discovery

In medical image datasets, discrete labels are often used to describe a continuous spectrum of conditions, making unsupervised image stratification a challenging task. In this work, we propose VAESim, an architecture for image stratification based on a conditional variational autoencoder. VAESim learns a set of prototypical vectors during training, each associated with a cluster in a continuous latent space. We perform a soft assignment of each data sample to the clusters and reconstruct the sample based on a similarity measure between the sample embedding and the prototypical vectors. To update the prototypical embeddings, we use an exponential moving average of the most similar representations between actual prototypes and samples in the batch size. We test our approach on the MNIST handwritten digit dataset and the PneumoniaMNIST medical benchmark dataset, where we show that our method outperforms baselines in terms of kNN accuracy (up to +15% improvement in performance) and performs at par with classification models trained in a fully supervised way. Our model also outperforms current end-to-end models for unsupervised stratification.
Generation-based contrastive model with semantic alignment for generalized zero-shot learning

Generalized zero-shot learning (GZSL) is an important research area in image computing, video processing, multimedia understanding, and other visual computing tasks. GZSL normally uses transferable semantic features to represent the visual features to predict unseen classes without training the unseen samples. The state-of-the-art zero-shot learning methods combine Generative Adversarial Network (GAN) and Contrastive Learning (CL) together to deeply transfer semantic features to visual features. However, the combined GAN module and CL module inevitably encounter the “semantic-visual inconsistent problem” in both the feature-generating process and the contrastive learning process. To handle the above problems, we propose the generation-based contrastive model with semantic alignment for generalized zero-shot learning. The proposed network is based on existing ZSL models combining GAN and CL, but with two additional alignment modules that are Feedback Alignment Module (FAM) and Negative sample Alignment Module (NAM). FAM applies an MLP (Multilayer Perceptron) to align the synthesized visual feature back to its semantic feature for keeping the semantic-visual consistency in the generator. NAM provides a new contrastive learning mechanism to align the negative pairs for keeping semantic-visual consistency during contrastive learning. Experimental results on massive real-world datasets show the proposed method achieves the new state-of-the-art in the field of generalized zero-shot learning. The source code of the proposed method is available athttps://github.com/yangjingqi99/GCSA.
Minimum spanning tree niching-based differential evolution with knowledge-driven update strategy for multimodal optimization problems

Multimodal optimization problems (MMOPs) aim to simultaneously locate as many global optima as possible with high accuracy. Recently, many niching strategies have been wildly used in evolutionary algorithms to solve MMOPs due to the advantage of maintaining the diversity of population. However, most multimodal algorithms are sensitive to the niching parameters and lack effective methods to update the “stagnant individuals”, including individuals that have trapped into local optima or converged to the same optima. In this paper, a minimum spanning tree niching-based differential evolution (TNDE) with knowledge-driven update (KDU) strategy is proposed to better solve the above challenges, where the “knowledge” includes historical evolutionary information, fitness distribution information, and individual distribution information. In TNDE, a minimum spanning tree niching (MSTN) strategy is proposed to adaptively divide the population, which can adjust the number of niches dynamically. Besides, the KDU strategy is proposed to utilize the knowledge to locate and update stagnant individuals. Lastly, an improved differential evolution with local stage-based mutation (LSM) and directional guidance selection (DGS) strategies is proposed to accelerate convergence and refine the accuracy of solutions, respectively. The comparison results with 16 state-of-the-art algorithms on CEC’2013 show that TNDE achieves significant advantages on high-dimensional problems or problems with many global optima.
SoftStep relaxation for mining optimal convolution kernel

An efficient convolution kernel could transform images into more expressive representations, which usually determines the quality of image classification models. Algorithms in the field of hyper-parameter optimization and neural architecture search could be applied to mining optimal kernels, while hyper-parameter optimization algorithms always require multiple training sessions to evaluate hyper-parameter configurations, resulting in a high time consumption. Recent gradient-based architecture search algorithms could deal with such problems within a single training session. However, there are currently no gradient-based NAS algorithms that could be used to efficiently optimize convolution kernels. This paper proposes a novel gradient-based algorithm, SoftStep, which could precisely and efficiently fine-tune the hyper-parameters of kernels. The proposed SoftStep is applied for the optimization of the convolution kernel’s key hyper-parameters and generalized to more hyper-parameters in deep learning. In experiments, architecture search tasks on multiple challenging datasets are compared to various state-of-art hyper-parameter optimization and architecture search algorithms. The reported results show that SoftStep could mine optimal kernels far more efficiently than other SOTA methods.
Re-interpreting rules interpretability

Abstract Trustworthy machine learning requires a high level of interpretability of machine learning models, yet many models are inherently black-boxes. Training interpretable models instead—or using them to mimic the black-box model—seems like a viable solution. In practice, however, these interpretable models are still unintelligible due to their size and complexity. In this paper, we present an approach to explain the logic of large interpretable models that can be represented as sets of logical rules by a simple, and thus intelligible, descriptive model. The coarseness of this descriptive model and its fidelity to the original model can be controlled, so that a user can understand the original model in varying levels of depth. We showcase and discuss this approach on three real-world problems from healthcare, material science, and finance.
Research on Target Tracking Algorithm of Micro-UAV Based on Monocular Vision

Aiming at the problem of limited payload and endurance of micro-UAV, the target tracking algorithm based on monocular vision is proposed. Since monocular vision cannot directly measure distance between the UAV and the target, triangulation and triangle similarity are used to calculate the distance information. Then, a target tracking method based on Kalman filter and KCF is designed. The tracking result of KCF is modified by Kalman filter to solve the problem of target occlusion. Finally, the position of the target in the world coordinate system is calculated through the coordinate transformation matrix, which is used to control the UAV for tracking the moving target. In order to verify the feasibility of the algorithm, target size estimation and target tracking algorithms are carried out. The experimental results show that the proposed algorithm can track the moving target effectively under the condition of short-term occlusion.
Detecting negative eigenvalues of exact and approximate Hessian matrices in optimization

Nonconvex minimization algorithms often benefit from the use of second-order information as represented by the Hessian matrix. When the Hessian at a critical point possesses negative eigenvalues, the corresponding eigenvectors can be used to search for further improvement in the objective function value. Computing such eigenpairs can be computationally challenging, particularly if the Hessian matrix itself cannot be built directly but must rather be sampled or approximated. In blackbox optimization, such derivative approximations are built at a significant cost in terms of function values. In this paper, we investigate practical approaches to detect negative eigenvalues in Hessian matrices without accessing the full matrix. We propose a general framework that begins with the diagonal and gradually builds submatrices to detect negative curvature. Crucially, our approach works both when exact Hessian coordinate values are available and when Hessian coordinate values are approximated. We compare several instances of our framework on a test set of Hessian matrices from a popular optimization library, and finite-differences approximations thereof. Our experiments highlight the importance of the variable order in the problem description, and show that forming submatrices is often an efficient approach to detect negative curvature.
The method of medium-scale mineralogical mapping during diamond searching: a case study in the Ygyatta Region (Western Yakutia)

The analysis of mineralogical information has enabled the development of a novel mineralogical zoning method using GIS and statistics. The proposed method was applied to the Ygyatta Region located within the Yakut Diamond-bearing Province. A set of mineralogical characteristics was chosen to examine the information content and productivity of the terrigenous collectors, providing an exact identification of the development areas of the deposits and the locations of their primary sources during medium-scale forecasting. The developed GIS-integrated database of the mineralogical passports of mine workings was based on the identification of background and anomalous parameters in the mineralogical field. The latter, within the Ygyatta Region, was delineated using ModelBuilder. The proposed method demonstrates the ability to contour mineralogical taxa of the “node” and “section” ranks within the Ygyatta Region, leading to the identification of six mineralogical nodes and fifteen mineralogical sections. This method can be effective for medium-scale zoning, based on mineralogical data from other diamond-bearing areas.
Sentence splitting in Arabic to Spanish translation

Abstract Modern Standard Arabic makes extensive use of coordination particles whereas punctuation marks are scarce and erratic, leading to long clauses. This is generally assumed to hinder Sentence Boundary Detection and to promote sentence splitting when translating from Arabic into English. Previous literature on translation from Arabic to Spanish is practically inexistent. We have tested this hypothesis regarding translation from Arabic to Spanish on a sample of 282,714 graphic words extracted from a bilingual corpus of 8,681,110 graphic words and found that each Arabic sentence yielded an average of 1.5 Spanish sentences. Furthermore, our data shows the potential impact of directionality in that sentence splitting when translating from Arabic into Spanish is 50% more frequent than from English into Arabic. We also determined statistically that five elements ( wa [ و ], ḥaythu [ حيث ], kamā [ كما ], wa-qad [ وقد ], and wa-dhalika [ وذلك ]) are the most salient potential markers for sentence splitting in the resulting Spanish translations. Our findings should be particularly interesting for Computational Linguistics and translator training.
Image Defogging based on Combined Sparse Gradient Minimization and CNN Architecture

Some environmental factors like haze or fog de-grades the quality of the image. These factors affect some real time processes such as object detection and recognition, automated vehicles and remote sensing which needs clear visible images for making critical decisions. Therefore, restoring the true image from the foggy image becomes significant. Now with the advancement in image processing, many image defogging and dehazing algorithms has been developed to improve the quality of the image. Many standard filtering techniques such as high boost filter, homomorphic filter can be used for image defogging but it fails to restore the foggy images completely so some advanced techniques like DCP, decomposition techniques, CNN based algorithms are used. The proposed work mainly focuses on CNN based algorithm for image defogging combined with SGM module for edge preservation. Image quality assessment (IQA) is done to measure the quality of the defogged image. These performance metrics mainly includes mean squared error (MSE), structural similarity index metric (SSIM),peak signal to noise ratio (PSNR).
Visible-Light Insulator Defect Detection Algorithm Based on Improved YOLOv4-Tiny

Insulator self-explosion defect detection plays a crucial role in the stable operation of power systems. In recent years, with the development of technology, deep learning-based object detection models have been widely used in power inspection work. To address the problems of redundant structure, large size, and slow detection speed of traditional object detection models, a lightweight insulator self-explosion defect detection algorithm based on YOLOv4-Tiny (You Only Look Once version 4-Tiny) is proposed. Firstly, the model training strategy of transfer learning is adopted to improve the model's generalization ability to the insulator dataset. Secondly, the SE attention module is introduced in the feature pyramid network to strengthen the model's feature extraction and fusion capability. Finally, a small target detection layer is added to enhance the model's ability to recognize small targets. Experimental results show that the average precision of the improved algorithm in this paper is 91.45%, which is 8.46% higher than the model using transfer learning. Additionally, the model size is 22.9MB, indicating that the model can achieve high detection accuracy for insulator and defect recognition, and the model's size is smaller than that of general object detection models, making it suitable for deployment on front-end devices to achieve real-time detection.
Facial Emotion Recognition based on Attentive Neural Network for the Blind

Facial expressions are considered to be a universal form of nonverbal communication. They convey countless emotions without even saying a word. These cues support speech by assisting the listener in deciphering the intended meaning of the words spoken. Unfortunately, due to the absence of access to such signs for the blind and visually impaired people (VIPs), interpersonal communication is hampered. For tackling this, a deep learning-inspired Facial Emotion Recognition (FER) model that recognizes facial emotions in real-time is proposed in this paper. The proposed Convolutional Neural Network (CNN) model was evaluated on the popular facial emotion recognition dataset of FER2013 using different performance evaluation metrics. Additionally, showcased it as a web application that runs the FER model in real-time to provide audio feedback to the visually impaired to interpret respective emotions.