Reviewerly/exHarmony huggingface dataset:
RBECA: A regularized Bi-partitioned entropy component analysis for human face recognition

This paper presents a novel approach for Human Face Recognition, namely Regularized Bi-partitioned Entropy Component Analysis (RBECA). This conservative approach regularizes the kernel entropy components by deterring the noise and affecting the lower entropy regions area, making the method robust to noise. The kernel feature space, formed by the kernel entropy component analysis (KECA), is divided into two partitions: the High Entropy Space (HES) and the Low Entropy Space (LES). The noise-laden low entropy spectrum is regularized by predicting entropy values obtained from the information-filled High Entropy Spectrum. The corresponding projection vectors are adjusted accordingly. A null space, comprising the negligible information and many dimensions, is eliminated using a Golden Search minimization function at two stages. The method retains the maximum entropy property and high recognition accuracy while using the optimum number of features. This resultant feature vector is classified using the cosine similarity measure. The algorithm is successfully tested on several benchmark databases like AR, FERET, FRAV2D, and LFW, using standard protocols and compared with other competitive methods. The proposed method achieves much better recognition accuracy than other well-known methods like PCA, ICA, KPCA, KECA, LGBP, ERE, etc., in all considered cases. Moreover, we have also proposed a CNN for the comparative analysis. For unbiased or fair performance evaluation, the sensitivity and specificity are also reported. • A noise robust RBECA method is proposed for human face recognition. • The proposed method requires a lower number of features than KECA. • The highest level of discriminatory information is retained. • The algorithm is trained and tested on several benchmark face databases. • A deep learning CNN framework is also implemented for comparative analysis.
Technology Advancement in Education &amp; Development of Smart Society

Education has the power to form how we experience the situations in this world. We just close our mind and open our eyes and way of thinking. In this crisis, smart and technovative innovations played a very significant role in making people aware about the situation, calm and encourage them to do positive action and to learn something new. Smart Education develops the flexibility among the learners to work in groups and explore smart ways of learning. It majorly focuses on the personalized, effortless and ubiquitous learning to uplift novice intelligence. It also allows the learners to enhance their practical knowledge.
Use of Alexnet Architecture in the Detection of Bone Marrow White Blood Cancer Cells

The cancer-affected area in white blood cell pictures is studied in this paper. AlexNet architecture is used to classify different types of white blood cells. This model, which was trained on cell pictures, first preprocesses the photos before extracting the best feature. The feature is extracted using the convolutional layer, Relu layer, and max pooling layer. For categorization of Eosinophils, Lymphocytes, Monocytes, and Neutrophils, the extracted features are input into a softmax and a completely linked layer. The various phases of image processing are used to improve the quality and accuracy of blood cancer detection. The accuracy of the blood sample image is determined by the affected area.
MS-ROCANet: Multi-Scale Residual Orthogonal-Channel Attention Network for Scene Text Detection

Deep neural networks-based scene text detection has obtained increasing attention in recent years. However, the existing scene text detection methods cannot effectively solve the problem of unclear text features. In this paper, a Multi-scale Residual Orthogonal-Channel Attention Network (MS-ROCANet) is proposed to improve the recall and accuracy of scene text detection. Specifically, a Detail-aware FPN is designed to capture more detailed information. Then, a Shared Composite Attention Head (SCAH) consists of a Residual Orthogonal Attention Module (ROAM), and a Residual Channel Attention Block (RCAB) is proposed. It can enhance textual region features at a multi-scale level. Finally, a global context extraction module is proposed to obtain global contextual information after referring to the core idea of Transformer. Extensive experiments demonstrated that our MS-ROCANet achieved competitive results on a variety of baselines for text detection. The codes and models are available at https://github.com/ASentry/MS-ROCANet
RETRACTED ARTICLE: Ecuadorian university English teachers’ reflections on emergency remote teaching during the COVID-19 pandemic

COVID-19 struck at the beginning of 2020, affecting, among others, the education industry. As a result, a lock-down quarantine was declared, and on-campus classes were suspended. Accordingly, emergency remote teaching (ERT) was set into motion to solve the education issue. This research aimed to obtain the reflections of 20 Ecuadorian polytechnic university English teachers on their experiences using ERT during two semesters. This paper is based on an explanatory sequential mixed-methods research design that used a Likert-scale survey and interviews to respond to the established research questions. The findings show that, in a general sense, teachers were not ready for the sudden shift to ERT, which generated feelings of anxiety. The most significant disadvantage reported was the extra workload caused by adapting materials and giving feedback to students. The study suggests that changing classes from on-campus to ERT was not easy to carry out at the beginning of the pandemic. There are practical implications for language department managers as it gives them light to prepare for the continuing pandemic and any other crisis that might require ERT to be in practice again.
Hybrid Loss Functions for Infrared and Visible Patch Matching using Siamese CNN

Image similarity between cross modal image pairs such as visual and thermal images is a challenging task which is yet to be comprehensively addressed by deep CNNs. The feature representations of images extracted from convolution layers must be generalized enough to differentiate between dissimilar and similar regions of diverse visual and thermal images. One of the training choices, loss functions, has a significant effect in performance of the trained CNN model. We demonstrate the usefulness of combined loss by applying it to find similarity between features learnt from convolution layers of a Siamese CNN model. The combination of loss functions fabricates the convolutional layered architecture/model to learn the various inherent spatial (or regional), correlated and intensity-based features of an image. Our experimentation shows that the performance of model both in terms of training error and in prediction depends on the choice of loss function and significantly improves in the case of hybrid loss function.
Semantic Code Search in Software Repositories using Neural Machine Translation

Abstract Nowadays, software development is accelerated through the reuse of code snippets found online in question-answering platforms and software repositories. In order to be efficient, this process requires forming an appropriate query and identifying the most suitable code snippet, which can sometimes be challenging and particularly time-consuming. Over the last years, several code recommendation systems have been developed to offer a solution to this problem. Nevertheless, most of them recommend API calls or sequences instead of reusable code snippets. Furthermore, they do not employ architectures advanced enough to exploit the semantics of natural language and code in order to form the optimal query from the question posed. To overcome these issues, we propose CodeTransformer, a code recommendation system that provides useful, reusable code snippets extracted from open-source GitHub repositories. By employing a neural network architecture that comprises advanced attention mechanisms, our system effectively understands and models natural language queries and code snippets in a joint vector space. Upon evaluating CodeTransformer quantitatively against a similar system and qualitatively using a dataset from Stack Overflow, we conclude that our approach can recommend useful and reusable snippets to developers.
PARMOREL: a framework for customizable model repair

Abstract In model-driven software engineering, models are used in all phases of the development process. These models must hold a high quality since the implementation of the systems they represent relies on them. Several existing tools reduce the burden of manually dealing with issues that affect models’ quality, such as syntax errors, model smells, and inadequate structures. However, these tools are often inflexible for customization and hard to extend. This paper presents a customizable and extensible model repair framework, PARMOREL, that enables users to deal with different issues in different types of models. The framework uses reinforcement learning to automatically find the best sequence of actions for repairing a broken model according to user preferences. As proof of concept, we repair syntactic errors in class diagrams taking into account a model distance metric and quality characteristics. In addition, we restore inter-model consistency between UML class and sequence diagrams while improving the coupling qualities of the sequence diagrams. Furthermore, we evaluate the approach on a large publicly available dataset and a set of real-world inspired models to show that PARMOREL can decide and pick the best solution to solve the issues present in the models to satisfy user preferences.
On Metadata Quality in Sceiba, a Platform for Quality Control and Monitoring of Cuban Scientific Publications

It is introduced a platform for quality control and monitoring of Cuban scientific publications named Sceiba. To this end, it needs to collect scientific publications comprehensively at the national level. Metadata quality is crucial for Sceiba interoperability and development. This paper exposes how metadata quality is assured and enhanced in Sceiba. The metadata aggregation pipeline is worked out to collect, transform, store and expose metadata on Persons, Organizations, Sources, and Scientific Publications. Raw data transformation into Sceiba’s internal metadata models includes cleaning, disambiguation, deduplication, entity linking, validation, standardization, and enrichment using a semi-automated approach aligned with the findability, accessibility, interoperability, and reusability principles. To meet the requirements of metadata quality in Sceiba, a three-layer structure for metadata is used, including 1) discovery metadata, which allows the discovery of relevant scientific publications by browsing or query, 2) contextual metadata, which allows a) rich information on persons, organizations and other aspects associated with publications, b) interoperation among common metadata formats used in Current Research Information Systems, journals systems or Institutional Repositories; 3) detailed metadata, which is specific to the domain of scientific publication evaluation. The example provided shows how the metadata quality is improved in the Identification System for Cuban Research Organizations, one of Sceiba´s component applications.
Online education during the COVID-19 pandemic: academic analytics in the Kazakh context

During the first wave of the COVID-19 pandemic, Kazakhstan followed strict global procedures. An important government decision was made to close schools and move to online learning. The sudden transition from traditional education to a distance learning format has caused some problems. In this regard, a comprehensive review of the problems of organizing online education during the first wave of the pandemic is important and significant. Therefore, there is now an immediate need for more detailed research in order to understand the factual effectiveness of various program measures during crisis conditions (pandemic, natural disaster, etc.) However, in the context of Kazakhstan, there is a limited amount of scientifically based research on the problems of organizing online learning during the pandemic, compared with studies by scientists from different countries. The novelty of the study is that in the article the authors pay special attention to the process of organizing online learning in the Kazakh context during the beginning of the pandemic, in order to give a realistic vision of a new way of learning in a crisis situation. Practical significance: this study complements the existing literature, demonstrating the role of online learning in supporting educational services in crisis situations.
Machine Learning Algorithms for Predicting the Graduation Admission

With the rise in the number of graduates wishing to continue their education, it has become more difficult for students to gain admission to their desired university. Typically, newly graduated students are unaware of the standards and procedures for postgraduate admission and may spend a significant amount of money seeking guidance from consulting firms to help them determine their prospects of admission. However, given the restricted number of colleges that a human consultant can assess, this technique may be biased and erroneous. Higher education at foreign universities often implies that we have a wide range of possibilities, such as Canada, the United States, the United Kingdom, Germany, Italy, and Australia. However, we are only interested in students who wish to pursue their master’s degree in America. Students who desire to pursue a master’s degree in America must take the GRE (Graduate Records Examination) and the TOEFL (Test of English as a Foreign Language) (Test of English as a Foreign Language). They must compose their SOP (statement of purpose) and LOR (letter of reference) after they have completed the tests, which is one of the most important elements they must consider. These letters of recommendation and statements of purpose are crucial if the student is applying for a scholarship. When it comes to applying to master’s programmers, prospective graduate students are constantly faced with a conundrum when picking which universities to attend. While many predictors and consultancies can help a student, they aren’t always accurate because admissions decisions are based on a small number of historical admissions. As a result, with the rising demand for higher education, one should not be confused about where to apply. The pupils must next choose which institutions they wish to attend or apply to; we cannot apply to all universities because that would result in a large number of application fees. Here’s the issue: the student has no idea which university he might be accepted to. Several internet blogs can help with these issues, but they aren’t always correct and don’t take all of the elements into account, and some consulting firms will take a lot of our money and time while occasionally providing inaccurate information. As a result, our goal is to create a model that would tell students their chances of being accepted into a specific university. This model should take into account all of the important aspects that play a role in the student admissions process and should be highly accurate.
An optimized FM-index library for nucleotide and amino acid search

Pattern matching is a key step in a variety of biological sequence analysis pipelines. The FM-index is a compressed data structure for pattern matching, with search run time that is independent of the length of the database text. Implementation of the FM-index is reasonably complicated, so that increased adoption will be aided by the availability of a fast and flexible FM-index library.We present AvxWindowedFMindex (AWFM-index), a lightweight, open-source, thread-parallel FM-index library written in C that is optimized for indexing nucleotide and amino acid sequences. AWFM-index introduces a new approach to storing FM-index data in a strided bit-vector format that enables extremely efficient computation of the FM-index occurrence function via AVX2 bitwise instructions, and combines this with optional on-disk storage of the index's suffix array and a cache-efficient lookup table for partial k-mer searches. The AWFM-index performs exact match count and locate queries faster than SeqAn3's FM-index implementation across a range of comparable memory footprints. When optimized for speed, AWFM-index is [Formula: see text]2-4x faster than SeqAn3 for nucleotide search, and [Formula: see text]2-6x faster for amino acid search; it is also [Formula: see text]4x faster with similar memory footprint when storing the suffix array in on-disk SSD storage.AWFM-index is easy to incorporate into bioinformatics software, offers run-time performance parameterization, and provides clients with FM-index functionality at both a high-level (count or locate all instances of a query string) and low-level (step-wise control of the FM-index backward-search process). The open-source library is available for download at https://github.com/TravisWheelerLab/AvxWindowFmIndex.
Layer Adaptive Deep Neural Networks for Out-of-Distribution Detection

During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels. While features at different layers could summarize the important factors of the inputs at varying levels, modern out-of-distribution (OOD) detection methods mostly focus on utilizing their ending layer features. In this paper, we proposed a novel layer-adaptive OOD detection framework (LA-OOD) for DNNs that can fully utilize the intermediate layers’ outputs. Specifically, instead of training a unified OOD detector at a fixed ending layer, we train multiple One-Class SVM OOD detectors simultaneously at the intermediate layers to exploit the full-spectrum characteristics encoded at varying depths of DNNs. We develop a simple yet effective layer-adaptive policy to identify the best layer for detecting each potential OOD example. LA-OOD can be applied to any existing DNNs and does not require access to OOD samples during the training. Using three DNNs of varying depth and architectures, our experiments demonstrate that LA-OOD is robust against OODs of varying complexity and can outperform state-of-the-art competitors by a large margin on some real-world datasets.
Subgraph Convolutional Network for Recommendation

Nowadays recommendation systems play an important role in our lives, which help users to quickly identify their desirable items. The networking trend for world (i.e., every sector of our world can be networked) has made the recommendation systems one of the intensively studied research areas in the last decades. In this paper, we formulate a graph-based recommendation problem, which aims to find the most relevant nodes for a given set of query nodes in the graph. For graph embedding, Graph Convolutional Network (GCN), which aggregates neighbor information via convolution layers, is an effective model. However, a convolution layer in a GCN only considers unstructured information, i.e., it takes single nodes as input and only leverages the first-order neighbor information, so only limited local information can be learned. To overcome the mentioned limitations, we develop a Subgraph Convolutional Network (SCN) model which aggregates both neighbor node information and structural information via convolution layers. Moreover, the fully connected layer based link prediction is integrated for effective recommendations. The experimental results on real-world datasets verify the effectiveness of our solution.
Crest factor reduction of spectrogram art signals based on clipping and filtering processes

Spectrogram art communications exchange visual messages drawn on sound signals. This study attempts to reduce the crest factor of spectrogram art signals to improve their visibility. This paper describes some experimental results examining our proposed technique that employs clipping and filtering processes.
Adversarial Attack Protection Scalar Multiplication for WSNs Resistance Machine-Learning Side-channel Attack

Wireless sensor networks (WSNs) have limited storage and low processing capabilities. However, these devices may be captured by opponents, so the security requirements are particularly strict. With the development of side-channel attacks based on the machine-learning, designing a safe elliptic curve scalar multiplication for computing-limited devices like WSNs has become a major challenge. Based on the adversarial attack technology, a secure scalar multiplication is proposed in this article. The main contributions are: (1) We propose an efficient non-zero form (NZF) encoding algorithm that can be applied to various types of elliptic curves; (2) we have designed a secure scalar multiplication algorithm that can resist against conventional side-channel attacks such as SPA, DA, DPA, RPA, and ZPA; and (3) we propose an adversarial protection mechanism based on blind point technology and NZF coding, which can prevent side-channel attacks based on machine learning. The algorithm has no precomputation and is suitable for low communication frequency, low calculation amount, and high security requirements. Especially, it can be applied to lightweight equipment such as WSN and IoT.
A Prediction Model for Bitcoin Cryptocurrency Prices

Cryptocurrencies like Bitcoin are a contentious and difficult technological innovation in today’s financial system. With huge improvements in financial markets, machine learning and artificial intelligence aided trading have piqued interest in recent years. This study suggests a predicting model for blockchain Bitcoin cryptocurrency prices and its profitability trading strategies using machine learning algorithms (ICA-Firefly and SVMs). For the prediction analysis of Bitcoin cryptocurrency data, this study combines ICA-Firefly with SVM algorithms. The model was tested on a large dataset of 2194 samples, and its performance was analyzed in terms of evaluation metrics. In evaluation to the state of the art, the ICA-Firefly with L-SVM and Sigmoid SVM classification approach performs well on the Bitcoin sample dataset, with an accuracy of 95% and 97%, respectively. The ICA-Firefly with the SVM model can be adopted as a viable financial system sustainability management strategy.
SSO: A Hybrid Swarm Intelligence Optimization Algorithm

Swarm intelligence is a captivating area in the field of optimization for the researchers. Many researchers have proposed several algorithms inspired from nature by analysing the swarming behaviour of various creatures like ants, animals, birds and more. A hybrid approach for optimization is proposed by modelling the swarm behaviour of spider monkeys and squirrels where both of these are nature-inspired algorithms for looking out food. The spider monkey optimization is coherent for training and is adaptable in swarm intelligence algorithms and enhanced computational speed. On the other hand, the squirrel search algorithm is inspired by squirrels which have a repetitive process for lifetime of searching for food, and this behaviour is formulated mathematically and using gliding technique for locomotion. With these two algorithms, a new algorithm is proposed named spider squirrel optimization, and it is compared with similar algorithms on different test problems and evaluated using convergence rate analysis and statistical analysis. The results demonstrate that the proposed algorithm shows promising convergence rate and mean absolute error as compared to other existing optimizers.