Reviewerly/exHarmony huggingface dataset:
MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation

Automated curation of noisy external data in the medical domain has long been in high demand, as AI technologies need to be validated using various sources with clean, annotated data. Identifying the variance between internal and external sources is a fundamental step in curating a high-quality dataset, as the data distributions from different sources can vary significantly and subsequently affect the performance of AI models. The primary challenges for detecting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data shifts</i> are - (1) accessing private data across healthcare institutions for manual detection and (2) the lack of automated approaches to learn efficient shift-data representation without training samples. To overcome these problems, we propose an automated pipeline called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MedShift</i> to detect top-level shift samples and evaluate the significance of shift data without sharing data between internal and external organizations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MedShift</i> employs unsupervised anomaly detectors to learn the internal distribution and identify samples showing significant shiftness for external datasets, and then compares their performance. To quantify the effects of detected shift data, we train a multi-class classifier that learns internal domain knowledge and evaluates the classification performance for each class in external domains after dropping the shift data. We also propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data quality metric</i> to quantify the dissimilarity between internal and external datasets. We verify the efficacy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MedShift</i> using musculoskeletal radiographs (MURA) and chest X-ray datasets from multiple external sources. Our experiments show that our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently.
Quantum-behaved particle swarm optimization with dynamic grouping searching strategy

The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC’2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems.
DIGITAL TOOLS FOR THE FORMATION OF ICT COMPETENCE IN FUTURE TEACHERS OF FOREIGN LANGUAGES

The article examines the peculiarities of the selection and use of digital tools for the formation of ICT competence of future teachers of foreign languages, in particular, their ability to create electronic educational resources. It was determined that the formation of ICT competence of future IM teachers should be carried out in an integrated manner during the study of normative and selective disciplines of professional and methodical training. Groups of digital tools are proposed, which are expedient to use for the formation of ICT competence in future teachers of foreign languages, namely tools: 1) for creating interactive electronic tasks (training, testing, games), which in the process of use contribute to the formation of foreign language learning subjects communicative competence and control of its formation; 2) visualization of educational material, which involves presenting it in a condensed and visual form, which is convenient for perception and deeper understanding; 3) implementation of synchronous and asynchronous communication in order to organize the educational process in foreign languages. The criteria for the selection of digital tools for the formation of ICT competence of future teachers of foreign languages are formulated and substantiated, and examples of their use for the formation of target competence are given.
Communication problems at the University of Goma "UNIGOM"

This practical work of the English seminary, presented in the setting of the Doctoral school of the university National Educational UPN-UNIGOM given by the Professor Ordinaire Mbukani carries on" the problems or obstacles of the communication in an academic institution ", case of the University of Goma. The choice of this academic institution is not a fact of the luck", like all communication was never at random ". Because the UNIGOM is the biggest official university that the city of Goma counts, curiously that knows several problems that affect his/her/its wanted picture by his/her/its publics.
A Heuristic Hybrid Instance Reduction Approach Based on Adaptive Relative Distance and k-means clustering

Abstract The authors have requested that this preprint be removed from Research Square.
Improving English Language Learning Skills Using Social Networking

This paper discussed efforts to improve English learning skills using social networking. The author believes that most students now learning English as a foreign language certainly need more opportunities to practice excellently to relax their learning. However, progress in learning English is often identified with fluency in communicating with fellow students, not in terms of fluency in speaking using various social media applications so that such an atmosphere presents an open feel for students in the form of not only communicating in friendship but still looking for robust learning solutions. This review tries to describe how the benefits of social networks are not only valuable as friendships but can improve English proficiency for students in Indonesia. The data that we found was then analyzed from various sources and perspectives, which were the results of secondary data in book publications, scientific papers, and websites that discussed foreign language learning issues using social media networks. Based on the study results and discussion, we conclude that foreign language learning is like a social network. The reason is that through social media, there are now many types of social media that can be used to learn English in an effective and relaxed manner, all of which provide a variety of features and content that make learning more polite but still helpful.
ON THE ISSUE OF DIGITAL TRANSFORMATION MANAGEMENT TOOLS AT THE UNIVERSITY BASED ON AN INDICATIVE ASSESSMENT OF DIGITAL MATURITY

The strategy of digital transformation of the higher education and science industry of the Russian Federation defines approaches to achieving the digital maturity of higher education organizations. The article discusses the barriers that hinder the digital transformation of the university, and ways to overcome them. Based on the indicative assessment of digital maturity of the Povolzhsky Institute of Management named after P. A. Stolypin, a branch of the RANEPA, management decisions are described that made it possible to achieve a higher level of digital maturity and determine the directions for further development of the university in terms of achieving the goal.
REGIONAL SYSTEM OF HIGHER ECONOMIC EDUCATION (ON THE EXAMPLE OF THE KIROV REGION)

The regional education system is a set of objects, connections, stable relationships between subjects aimed at solving regional educational goals and objectives. The regional system of higher economic education is a system of interrelated elements (stages) of education, one of which is the magistracy. To date, the master’s program in the areas of preparation of the enlarged group “Economics and Management” is faced with a number of risks, the occurrence of which adversely affects the entire regional system of higher economic education.
SalDA: DeepConvNet Greets Attention for Visual Saliency Prediction

Predicting salient regions in images requires the capture of contextual information in the scene. Conventional saliency models typically use the encoder-decoder architecture and multi-scale feature fusion for modeling contextual features, which, however, possess huge computational cost and model parameters. In this paper, we address the saliency prediction task by capturing long-range dependencies based on the self-attention mechanism. Self-attention has been widely used in image recognition or other classification tasks, but is still rarely being considered in regression based saliency prediction task. Inspired by non-local block, we propose a new SALiency prediction network in which DeepConvNet is integrated with the Attention mechanism, namely SalDA. Considering each feature map may capture different salient regions, our spatial attention module firstly adaptively aggregates the feature at each position by a weighted sum of the features at all positions within each independent channel. Meanwhile, in order to capture interdependence between channels, we also introduce a channel attention module to integrate different features among different channels. We combine these two attention modules into a multi-attention module to further improve the saliency map prediction for the network. We show the effectiveness of SalDA on the largest saliency prediction dataset SALICON. Compared to other state-of-the-art methods in this area, we can yield comparable saliency prediction performance, but with substantially less model parameters and shorter inference time.
A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques

The Internet has a huge amount of information when it comes to analysis, much of which is valuable and significant. Arabic Sentiment Analysis (SA) is a method responsible for analyzing people’s thoughts, feelings, and responses to a variety of products and services on social networking and commercial sites. Several researchers utilize sentiment analysis to determine the opinions of customers in various areas, including e-marketing, business, and other fields. Deep learning (DL) is a useful technology for developing sentiment analysis models to improve e-marketing operations. There are a few studies targeting Arabic sentiment analysis (ASA) in e-marketing using deep learning algorithms. Due to a number of difficulties in the Arabic language, such as the language’s morphological features, the diversity of dialects, and the absence of suitable corpora, sentiment analysis on Arabic material is restricted. In this paper, we will compare several Arabic sentiment analysis models. Also, we discuss the deep learning algorithms that are employed in Arabic sentiment analysis. The domain of the collected papers is Arabic sentiment analysis in e-marketing using deep learning. Our first contribution is to introduce and present deep learning models that are used in ASA. Secondly, investigate and study Arabic datasets utilized for Arabic sentence analysis. We create and develop a new Arabic dataset for Saudi Arabian communication companies, namely Sara-Dataset, to increase the quality and quantity of their services. Third, each collected study is assessed in terms of its methodology, contributions, deep learning techniques, performance, Arabic datasets in emarketing, and potential improvements in developing Arabic sentiment analysis models. Fourth, we analyzed several papers’ performance in terms of accuracy, F-measure, recall, pre-procession, and area under the curve (AUC). Also, our comparative analysis includes feature selection (e.g., domain-specific selection) methods that are used in Arabic sentiment analysis. Fifth, we also discuss how to improve Arabic sentiment analysis using preprocessing techniques (e.g., word embedding). Finally, we provide a design model for analyzing Arabic sentiment about communications services provided by Saudi Arabian enterprises.
Theoretical Foundations for Pseudo-Inversion of Nonlinear Operators

The Moore-Penrose inverse is widely used in physics, statistics, and various fields of engineering. It captures well the notion of inversion of linear operators in the case of overcomplete data. In data science, nonlinear operators are extensively used. In this paper we characterize the fundamental properties of a pseudo-inverse (PI) for nonlinear operators. The concept is defined broadly. First for general sets, and then a refinement for normed spaces. The PI for normed spaces yields the Moore-Penrose inverse when the operator is a matrix. We present conditions for existence and uniqueness of a PI and establish theoretical results investigating its properties, such as continuity, its value for operator compositions and projection operators, and others. Analytic expressions are given for the PI of some well-known, non-invertible, nonlinear operators, such as hard- or soft-thresholding and ReLU. Finally, we analyze a neural layer and discuss relations to wavelet thresholding.
Collaborative 3D Object Detection for Autonomous Vehicles via Learnable Communications

3D object detection from LiDAR point cloud is a challenging task in autonomous driving systems. Collaborative perception can incorporate information from spatially diverse sensors and provide significant benefits for accurate 3D object detection from point clouds. In this work, we consider that the autonomous vehicle uses local point cloud data and combines information from neighboring infrastructures through wireless links for cooperative 3D object detection. However, information sharing among vehicles and infrastructures in predefined communication schemes may result in communication congestion and/or bring limited performance improvement. To this end, we propose a novel collaborative 3D object detection framework using an encoder-decoder network architecture and an attention-based learnable communications scheme. It consists of three components: a feature encoder network that maps point clouds into feature maps; an attention-based communication module that propagates compact and fine-grained query feature maps from the vehicle to support infrastructures, and optimizes attention weights between query and key to refine support feature maps; a region proposal network that fuses local feature maps and weighted support feature maps for 3D object detection. We evaluate the performance of the proposed framework on CARLA-3D, a new dataset that we synthesized using CARLA for 3D cooperative object detection. Experimental results and bandwidth consumption analysis show that the proposed collaborative 3D object detection framework achieves a better detection performance and communication bandwidth trade-off than five baseline 3D object detection models under different detection difficulties.
Fusion-Embedding Siamese Network for Light Field Salient Object Detection

Light field salient object detection (SOD) has shown remarkable success and gained considerable attention from the computer vision community. Existing methods usually employ a single-/two-stream network to detect saliency. However, these methods can only handle up to two different modalities at a time, preventing them from being able to fully explore the rich information in multi-modal light field derived data. To address this, we propose the first joint multi-modal learning framework, called FES-Net, for light field SOD, which can take rich inputs not limited to two modalities. Specifically, we propose an attention-aware adaptation module to first transform the multi-modal inputs for use in our joint learning framework. The transformed inputs are then fed to a Siamese network along with multiple embedded feature fusion modules to extract informative multi-modal features. Finally, we predict saliency maps from the high-level extracted features using a saliency decoder module. Our joint multi-modal learning framework effectively resolves the limitations of existing methods, providing efficient and effective multi-modal learning that can fully explore the valuable information in light field data for accurate saliency detection. Furthermore, we improve the performance by introducing the Transformer as our backbone network. To the best of our knowledge, the improved version of our model, called FES-Trans, is the first attempt to address the challenging light field SOD with the powerful Transformer technique. Extensive experiments on benchmark datasets demonstrate that our models are superior light field SOD approaches and outperform cutting-edge models remarkably.
Source-Filter-Based Generative Adversarial Neural Vocoder for High Fidelity Speech Synthesis

This paper proposes a source-filter-based generative adversarial neural vocoder named SF-GAN, which achieves high-fidelity waveform generation from input acoustic features by introducing F0-based source excitation signals to a neural filter framework. The SF-GAN vocoder is composed of a source module and a resolution-wise conditional filter module and is trained based on generative adversarial strategies. The source module produces an excitation signal from the F0 information, then the resolution-wise convolutional filter module combines the excitation signal with processed acoustic features at various temporal resolutions and finally reconstructs the raw waveform. The experimental results show that our proposed SF-GAN vocoder outperforms the state-of-the-art HiFi-GAN and Fre-GAN in both analysis-synthesis (AS) and text-to-speech (TTS) tasks, and the synthesized speech quality of SF-GAN is comparable to the ground-truth audio.
TIME-CONSUMING FITNESS FUNCTIONS APPROXIMATION FOR GENETIC ALGORITHMS PERFORMANCE IMPROVEMENT

Genetic algorithms are one of the most efficient meta-heuristics. The base of genetic algorithms is a set of candidate optimal solutions called population. The initial population usually is randomly generated. Optimization goes in epochs of new generations. New generations are produced after crossover and mutation. Mating between individuals is done after the selection of the better-fitted individuals. The fitness value of each individual is calculated by supplying the candidate solution to the optimized target function. The efficiency of the genetic algorithms is tightly related to the fast calculation of the fitness value. When the target function is a very timeconsuming one the efficiency of the genetic algorithms falls dramatically. This study proposes target function replacement by approximation function based on curve fitting.
DISCOVERING BEAUTY, RECOGNIZING HUMAN. A GLANCE AT WAYS, TOOLS, AND AIMS FOR TEACHING ART

Does teaching art and its history to the new generations still make sense today, and how? Whether it�s figurative arts or crafts, what humanity has produced throughout its history is one of the things that most fascinate scholars: in the end, as it�s for philosophy about the development of thought, studying art is a way of looking for the reasons for human specificity. The beauty search could be intended as one of the most evident aspects of it: times, modes, and tools have changed through the decades, but what has not changed are the intrinsic demands that push mankind to pursue it and create horizons of meaning through art. Nowadays, digital technologies can help us not only in teaching or studying art but even more simply in the fruition of artworks not accessible to everyone, and it is something amazing for the opportunity that it represents. Unfortunately, often the limit of these tools is to stop at the surface of things, providing partial information concerning the iconographic and formal description, rather than guiding the user to a deeper comprehension of the reasons from which the artworks came. The question, then, is how teachers above all, but also other operators of the art world could better address the use of digital, to emphasize the actuality of the need to make art and express one�s own creativity, helping them to feel renowned artists of the past and the presents not such as deities, but close to us, according to our sensitivity.
Research on Distance Preservation Ability of Fault Location Features for Data-driven Dynamic Security Assessment

In the feature expression of fault location in the data-driven dynamic security assessment, Euclidean distance is generally used to measure the distance between features. However, Euclidean distance cannot accurately reflect the electrical distance relationship between the described fault locations, which limits the further improvement of the accuracy of data-driven dynamic security assessment. This paper proposes the concept of strict distance preservation of fault location features. Based on the fault location feature expression based on the electrical coordinate system, the mathematical definition of strict distance preservation of electrical coordinates is constructed. Based on the distance metric of p-norm, the conjecture that Chebyshev distance is the only distance metric to satisfy strict distance preservation is proposed and proved. Furthermore, the relationship between the distance preservation ability of electrical coordinates and p-norm is analyzed under the Chebyshev distance metric. Taking the transient stability crucial clearing time as an example, the contribution of the preservation ability of electrical coordinates to the accuracy of dynamic security assessment is verified based on K-nearest neighbor algorithm.
Energy-Aware Virtual Machine Integration based Task Scheduling for Green Data Centers

Currently, cloud computing and big data technology are developing rapidly worldwide, and data centers are springing up. However, with the construction of the data center in full swing, the problems of low utilization of server resources and high energy consumption are becoming increasingly prominent. In this regard, a minimum slack-time task scheduling method (MSTS) based on the single threshold virtual machine integration strategy is proposed in this paper, which can reduce energy consumption effectively while ensuring the task completion rate. The experimental verification of the MSTS method is carried out on the CloudSim simulation platform through a comparative study against the benchmark solutions, and the numerical results demonstrated the effectiveness of the proposed method in the promotion of energy efficiency compared with the existing solutions.