Reviewerly/exHarmony huggingface dataset:
Ethnopedagogical competence of students as a factor in the implementation of the ethnocultural education trend

The purpose of the study is to determine the need to study the ethnocultural trend in national education, its place at the state level, especially as a national-regional component. The author of the article substantiates the reasons and patterns of development of the ethnocultural trend of education in Russia as a multinational country and substantiates its role in formation of national unity due to inclusion of the ethnopedagogical component of training the future school teachers and university teachers in the educational environment. The scientific novelty of the study lies in the contrastive analysis of modern statistics of demographic data, the national composition of the Russian Federation; legislative acts stimulating development of ethnocultural trends in Russia; description of the nationalregional component as a fundamental element in the education system of the Republic of Karelia, clarification of the similarities and differences between the terms “ethnocultural competence” and “ethnopedagogical competence”. As a result, the practical experience of the flagship university of Karelia - Petrozavodsk State University - is presented as an organization that forms the ethnopedagogical competence of students and a leading platform that implements the principles of ethnocultural education in the system of education and upbringing in the region, indicating examples of ways to develop the ethnocultural trend of education in Russia.
A Coverage-Guided Fuzzing Framework based on Genetic Algorithm for Neural Networks

Due to the inherent difference between neural network and traditional software, it is very difficult to test it. At present, the use of fuzzing methods may be an effective exploration direction. We choose coverage-guided fuzzing as a method to test neural networks, and use neuron coverage as a coverage metric during execution. The effectiveness of neuron coverage will be demonstrated through experiments. On this basis, we designed a genetic algorithm-based fuzzing framework for neural networks, attempting to achieve greater coverage in a shorter time. And through the method of experimental comparison, the test efficiency of the framework is verified.
Sunday-FL – Developing Open Source Platform for Federated Learning

Since its inception, in approximately 2017, federated learning became an area of intensive research. Obviously, such research requires tools that can be used for experimentation. Here, the biggest industrial players proposed their own platforms, but these platforms are anchored in tools that they “promote”. Moreover, they are mainly "all-in-one" solutions, aimed at facilitating the federate learning process, rather than supporting research “about it”. Taking this into account, we have decided to start developing an open source modular flexible federated learning platform. The aim of this contribution is to briefly summarize key aspects of federated learning and, in this context, to introduce our platform.
Adversarial Cross-domain Community Question Retrieval

Community Q&amp;A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&amp;A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a question retrieval function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&amp;A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely, X-QR , which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&amp;A forums question retrieval. The experiment results suggest that the proposed X-QR model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&amp;A tasks.
Getting the ROC into Sync

Judging the conformity of binary events in macroeconomics and finance has often been done with indices that measure synchronization. In recent years, the use of Receiver Operating Characteristic (ROC) curve has become popular for this task. This paper shows that the ROC and synchronization approaches are closely related, and each can be represented as a weighted average of correlation coefficients between a set of binary indicators and the target event. An advantage of such a representation is that inferences on the degree of conformity can be made robust to serial dependence in the underlying series in the standard framework of a linear regression model. Such serial correlation is common in macroeconomic and financial data.
Model Optimisation Techniques for Convolutional Neural Networks

Deep neural networks provide good results for computer vision tasks. This has been possible due to a renewed interest in neural networks, availability of large-scale labelled training data, virtually unlimited processing and storage on cloud platforms and high-performance clusters. A convolutional neural network (CNN) is one such architecture better suited for image classification. An important factor for a better CNN performance, besides the data quality, is the choice of hyperparameters, which define the model itself. The model or hyperparameter optimisation involves selecting the best configuration of hyperparameters but is challenging because the set of hyperparameters are different for each type of machine learning algorithm. Thus, it requires a lot of computational time and resources to determine a better performing machine learning model. Therefore, the process has a lot of research interest, and currently a transition to a fully automated process is also underway. This chapter provides a survey of the CNN model optimisation techniques proposed in the literature.
Bio-inspired algorithms

Bio-inspired computing is a field of study that brings together the areas of computer science, biology, and mathematics; it pursuits two closely related goals: the use of computers to model living phenomena and the use of these phenomena as a source of inspiration for optimizing the use of computing resources. The growing number of bio-inspired optimization algorithms gaining notoriety each year is proof of this approach's efficiency. One of the characteristics common to many bio-inspired algorithms is the capability to deal with high-dimensional non-linear problems, such as those commonly found in biosignal analysis. For this reason, knowing and understanding the basic mechanism behind these algorithms is a must for experts working with biosignals. This chapter presents an overview of six bio-inspired algorithms, namely genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search (CS), artificial bee colony (ABC), and flower pollination algorithm (FPA). All six algorithms are well known for their effectiveness in dealing with high-dimensional cost functions, and together offer a fair panorama on bio-inspired optimization's state of the art.
Learning Robotic Skills via Self-Imitation and Guide Reward

Reinforcement learning (RL) has been extensively studied for robotic skill acquisition. Nevertheless, existing methods require extensive environmental interactions or high-quality demonstrations, which limits their application in practice. To alleviate this problem, a practical algorithm, named self-imitation learning with guide reward (SILGR), is proposed. The algorithm selects relatively good trajectories as expert data instead of external demonstrations and then assigns a guide reward to each transition. The criterion of the guide reward generator improves consistently with the evolution of the agent. In this way, the agent explores the environment in a task-relevant direction and exploits the experience more effectively, improving sample efficiency and performance. The results on four continuous locomotion tasks indicate that the proposed scheme achieves better performance than other state-of-the-art deep RL methods.
Visible Watermark Removal Based on Dual-input Network

A visible watermark removal network that combines partial and standard convolution is proposed in this paper. Existing deep learning-based watermark removal methods use a standard convolutional network over the watermarked image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the watermark pattern covered area (typically the mean value). When the watermark is almost opaque, this often leads to artifacts such as color discolorepancy and blurriness. Based on this, we propose a watermark removal network that uses partial convolution, where the convolution is masked and re-normalized to only be conditioned on valid pixels, and then use a dual-input branch network based on ordinary convolution for optimization, one of the input branches is connected to the partial convolution network, and the other accept the watermarked image directly. With this mechanism, different convolutions can be combined to remove watermarks with different transparency. The scheme is evaluated through a large watermark image dataset. Experimental results show that this method can effectively remove watermarks of different colors and transparency.
An Iterative Strategy for Deep Learning Classification on Spatial Data Streams

Although the classification of a static set of spatial objects has been well studied, the classification of spatial objects generated by a spatial data stream has not. In this paper, we propose an iterative deep learning strategy for spatial data stream classification. Using a deep neural network, our strategy iteratively performs training and testing of the classifier, with the goal of reaching a desired accuracy, and the same accuracy that would be achieved as a classifier that is trained and tested with the entire object set. An experimental evaluation of our strategy versus a fully trained classifier with varying training and testing splits shows that the higher the percentage of training objects, the better the accuracy, although the other splits do converge closer to the accuracy of the fully trained classifier.
iTouch – Blind Assistance Smart Glove

Differentially abled people need support of smart assistive devices, of which visually impaired people need at most care. There are several wearable devices and mobile apps to assist them, but they are unable to meet the sense of vision. It would be better if wearable devices won&#x2019;t hurt them and keeping cell phones in our pockets than having them in hands. iTouch is a combination of "Smart Glove + Your Phone + Bone Conductive Headphone". Together Intelligent Glove and Cell Phone, they have capability to Call, Text, Record Video and Audio, Capture Image, Object Detection, Stair Case Detection, Human Detection, Presence of electricity, GPS Navigation, Heart Beat Monitoring and lot more. Glove can be used to access apps in the Mobile. Since thumb is the pointer, only one hand is needed. Bone conductive head phone can be used to listen to music, answer a call and also it will help to hear our surrounding than ears are closed. Glove is flexible and too thin enough to do our daily activities.
MACHINE LEARNING: AN OVERVIEW

Given the tremendous availability of data and computer power, there is a resurgence of interest in using data driven machine learning methods to solve issues where traditional engineering solutions are hampered by modeling or algorithmic flaws. The purpose of this article is to provide a comprehensive review of machine learning, including its history, types, applications, limitations and future prospects. In addition to this, the article also discusses the main point of difference between the field of artificial intelligence and machine learning.
Experiment Research on Spam Filter Classifier Based on Naive Bayesian Algorithm

These days, we are bombarded with endless spam e-mails. The harm of spam to our daily life includes: spam usually has unhealthy contents, which requires a vast waste of bandwidth to transfer spam, and vast waste of space to store spam. In this paper, main technologies to identify and block spam are analyzed including information filtering, blacklist and white list, and intention analysis. Then a classifier based on naive Bayesian algorithm to determine whether an e-mail is a spam or not is given. Finally, experiments of different settings are conducted and analyzed to show how different settings affects the accuracy, precision, recall, and f1_ score of dataset.
Graph signal denoising using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1179" altimg="si272.svg"><mml:mi>t</mml:mi></mml:math>-shrinkage priors

Graph signal denoising problem is studied by estimating a piecewise constant signal over an undirected graph. A new Bayesian approach is proposed that first converts a general graph to a chain graph via the depth first search algorithm, and then imposes a heavy-tailed t-shrinkage prior on the differences between consecutive signals over the induced chain graph. Posterior computation can be conveniently conducted by fully exploring the conjugacy structure in the model. Posterior contraction rate is derived for a general class of Bayesian shrinkage estimators under suitable assumptions, including the proposed t-shrinkage prior as a special case, and it is shown that this rate is optimal up to a logarithmic factor, besides automatically adapting to the unknown edge sparsity level of the graph. The excellent empirical performance of the proposed method is demonstrated via extensive simulation studies and applications to stock market data and signal denoising over large real-world networks.
Box Regression-Guided Anchor-free for Robust Visual Tracking

The Siamese tracker-based approach has achieved significant success in recent years. However, these approaches do not consider the different requirements for input feature in classification and regression branches. The regression branch needs feature information slightly larger than the object region, while the classification branch needs to avoid classification failure caused by the introduction of background information. In this paper, we present a novel Box Regression-Guided Anchor-free for Robust Visual Tracking. Firstly, a scale-aware regression module is designed to satisfy the feature requirements of the regression branch, which can capture feature information of various scales. Secondly, regression-guided classification module is applied to aligning the feature between the regression result and correlation feature, thereby avoiding the introduction of background information to classification branch. In addition, the new correlation operation is introduced to gain more superb correlation feature. Comparsion experimental exhibits that the proposed tracker achieves promising results in five challenging benchmark tests, including GOT-10K, OTB-2015, VOT-2018, VOT-2019 and TrackingNet, and run at an average speed of 60 FPS in real-time.
Predicting Financial Suspicious Activity Reports with Online Learning Methods

This paper studies the problem of applying artificial intelligence (AI) methods to the detection of financial Suspicious Activity Reports (SARs). Financial institutions use semi-automated systems based on predetermined rules to identify suspicious activities, but most of the generated alerts are false positives and do not lead to a reporting to the authorities. From the initial investigation based on a predefined triggering scenario, to the decision of a case worth reporting to the authorities, critical parts of the process are carried out by human analysts. Part of a collaborative project with HSBC, the main objective of this research is to automate the identification of truly suspicious transactions by reducing the need for human intervention, as well as limiting the number of false alerts, using Neural Networks (NNs). We implement a prediction framework based on a combination of two unsupervised NNs used in an online learning mode and we compare the performances of our solution against other Machine Learning (ML) models.
Movie Recommendation System Using Deep Autoencoder

A Recommender system is a useful engine to predict things according to our interests. This paper explains the recommendation system which is based on the State of the Art Deep AutoEncoders which comes under Model-based filtering technique. Deep AutoEncoders are Artificial Neural Network that is used in Computer Vision and NLP. Here Deep AutoEncoders are used to find Top N recommendation of movies by an Unsupervised learning method. Since the patterns are found using Collaborative Filtering, Netflix three months dataset with implicit values have been used for evaluating the proposed recommender system. This paper is good at predicting the ratings missing from the sparse matrices which is initially provided as input. 20 models have been designed and the best model is linked with the frontend. The RMSE value for the training set and test set is 0.8686 and 0.9365 respectively which tells that this model does not overfit. It is thus proved that Deep AutoEncoders generalizes well.
Hybrid Mammogram Segmentation Using Watershed and Region Growing

The present paper introduces a new hybrid approach for mass segmenting in mammography images. In this work, special attention is paid to breast mass as they are the most common sign of cancer in mammograms. The human radiology experts analyze the mammograms manually to identify the malignancy of a mass. However, such a task takes time and depends on the decision of an expert, which can be affected by many factors. Therefore, the computer-aided diagnosis systems (CAD) in mammography are increasingly used to help radiologists in their decisions. Segmentation is an essential step in (CAD) systems. The proposed method, aiming to come up with a better mass segmentation, uses both watershed transformation technique and region growing algorithm. The watershed transformation was applied in the initial segmentation of the pretreated image while the region growing algorithm was applied in the final segmentation of the mass. We have tested our approach on mammogram images taken from mini-MIAS database. The suggested method can be put to the test through a quantative evaluation based on sensitivity, specificity, accuracy and overlap. The results demonstrate that the proposed approach could produce a precise mass edge.
Identification of Architecturally Significant Non-Functional Requirement

Software requirements which are significant for designing Software architecture are called architecturally significant requirements (ASR). If ASR is not correctly identified, the resulting architecture will not be good. Wrongly designed software can't achieve the desired goal and quality, and this eventually lead to the complete failure of the software. Due to the complex behaviors behind architectural requirements, identifying the correct requirement is complex even for experienced architects. Identification and classification of ASR using machine learning algorithms have been reported in the past. However, their work didn't include Non-functional requirements (NFR) which have more impact than the ordinary NFR that have little effect on the architecture. The significancy of NFR vary from system to system. In this study, we have built a machine learning model for the identification of architecturally significant non-functional requirements (ASNFR) for a real-time system from the SRS document. The proposed model used three machine learning techniques: support vectored machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN) using feature extraction techniques TF-IDF and software engineering pre-trained word2vec model. Grid search cross-validation techniques are used to tune the optimal value of hyperparameters of algorithms. We have prepared our own dataset and used 10 fold stratified cross-validation for evaluating and comparing the model. ASNFR identification model predicts 88% accuracy using SVM with TF-IDF and 87% in NB and KNN using TF-IDF and it predicts 73%, 70%, and 75% using SVM, NB, and KNN with pre-trained word2vec respectively. SVM with TF-IDF outperforms the others for the identification of ASNFR.
Maturity of information systems security in selected private Banks in Ethiopia

Information system security is more critical than ever before because security threats are rapidly growing and the environment requires organizations to continuously adapt to changes. Before putting in place information systems security measures, organizations are required to determine the maturity level of their information security governance. Extant literature reveals that there is no recent study on information systems security maturity level of banks in Ethiopia. This study, thus, seeks to measure the existing maturity level and examine the security gaps in order to propose possible changes in Ethiopian private banking industry's information system security maturity indicators. Four private banks are selected as a representative sample. SSE-CMM (System Security Engineering Capability Maturity Model) is used as the maturity measurement criteria and the measurement was based on ISO/IEC 27001 information security control areas. The data for the study was gathered using a questionnaire. A total of 93 valid questionnaires were gathered from 110 participants in the study. Based on the SSE-CMM maturity model assessment criteria, the private banking industry's current maturity level is level 2 (repeatable but intuitive). Institutions have a pattern that is repeated when completing information security operations, but its existence was not thoroughly proven, and institutional inconsistency still exists. Recommendations are forwarded for management intervention in order to address the identified gaps.
Commonsense Validation for Arabic Sentences using Deep Learning

Arabic language processing is one of the largest challenges faced by researchers due to the complex nature of the Arabic language traits. With the recent advancements in deep learning applications and the vast amounts of data available, it is possible to secure human-like performance in several tasks. One of these tasks is distinguishing the common-sense in sentences in different languages in general, and in Arabic in specific. The Arabic commonsense validation is still a growing area in tackling the Arabic language understanding domain. In this paper, a set of multilingual pre-trained transformer models were employed. These models were trained and tested using a dataset of the commonsense understanding in the Arabic field. The dataset contains 12 thousand pairs of sentences. The two sentences, in every pair, are very similar in syntax; however, one of these sentences is logical (makes sense) and the other is illogical. BERT, XLM-MLM and XLM-Roberta models were fine-tuned to discover the logical sentences in the sentence pairs. The best result was achieved using XLM-Roberta with an accuracy equals to 81.2%.