Reviewerly/exHarmony huggingface dataset:
A Project Tracking Tool for Scrum Projects with Machine Learning Support for Cost Estimation

Cost estimation in software development is very important because it not only gives an idea to all stakeholders on how long it takes to complete the product under development, but it also mandates tracking development activities so that the project does not overrun on time or budget. Several cost estimation models have been reported in the literature for software development using traditional life cycle models but there are only a few ad hoc methods for software projects that used agile methods. This paper describes the design and implementation of a project tracking tool for software projects that are developed using the agile method Scrum. The users of the tool can closely monitor the progress of user stories, sprint tasks and test cases inducted into a scrum board. The tool also supports cost estimation of the project based on user stories and sprint tasks. For every user story, the tool provides a measure of hardship to implement in terms of story points, and for every sprint task, it gives the anticipated completion time. The tool uses machine learning support for continuous monitoring of efforts based on sprint tasks. The effectiveness of the tool has been tested using three different graduate course projects.
Cyber Security in Banking Sector

Because banks are very often target of a cyber-attack, they have also good security controls in place. This paper analysis modern threats to banks and proposes an approach to detect and visualize the risk of data leakage. In the first part of this paper, a comparative analysis of the most common threats to the banking sector is made, based on both bank reports and cyber security companies. The authors came to the conclusion that at the bottom line, insider knowledge is necessary, which is the result of data leakage. This paper comparatively analysis modern threats to banks and shows an approach to detect and visualize the risk of data leakage. In the second part of the paper, a model - based on network graph - that can enumerate the risk of data leakage is proposed. Graphing a network of an organization with the connections of data flow between assets and actors can identify insecure connections that may lead to data leakage. As is demonstrated in this paper, financial institutions are important targets of cyber attacks. Consequently, the financial sector must invest heavily in cybersecurity and find the best ways to counter cyber attacks and cyber bank robbery attempts.
Product-Based Learning Adaptation to an Online Autonomous Work Strategy in Restriction Conditions by Covid-19

The pandemic, declared by the world health organization in March 2020 due to Covid-19, caused a commotion in all areas worldwide.Regarding education, in most countries it caused the immediate closure of schools and the interruption of face-to-face classes.This situation forced educational institutions to continue their teaching in an improvised way through a virtual system.Given these circumstances, the use of active teaching and learning methodologies that are adaptable to a virtual education scenario becomes necessary.In this framework, the present work proposes an adaptation of the Product-Based Learning methodology to an online work environment, so that it is used by teachers as a teaching-learning strategy, where students can create digital educational resources such as part of their autonomous work.Additionally, the products developed may be part of the learning outcomes of university students in times of confinement and social distancing.
Diversity in Ensemble Model for Classification of Data Streams with Concept Drift

Data streams can be defined as the continuous stream of data in many forms coming from different sources. Data streams are usually non-stationary with continually changing their underlying structure. Solving of predictive or classification tasks on such data must consider this aspect. Traditional machine learning models applied on the drifting data may become invalid in the case when a concept change appears. To tackle this problem, we must utilize special adaptive learning models, which utilize various tools able to reflect the drifting data. One of the most popular groups of such methods are adaptive ensembles. This paper describes the work focused on the design and implementation of a novel adaptive ensemble learning model, which is based on the construction of a robust ensemble consisting of a heterogeneous set of its members. We used k-NN, Naive Bayes and Hoeffding trees as base learners and implemented an update mechanism, which considers dynamic class-weighting and Q statistics diversity calculation to ensure the diversity of the ensemble. The model was experimentally evaluated on the streaming datasets, and the effects of the diversity calculation were analyzed.
Future of Scientific Communications and Scientific Information

The possibility of forecasting the development of scientific communications against the background of current trends, as well as the proposed projects is considered. Among these trends are digitalization, transition to open science, modification of scientific libraries, the state of existing information resources and prospects for integration and development, including within the framework of the concepts of “knowledge space”. The determining place for the development of scientific communications belongs to the system of evaluation of scientific results, and the current system based on bibliometric methods is considered unsatisfactory. The differentiation of scientific information on publications and data is indicated. Economic, legal and linguistic aspects of scientific communication are briefly discussed.
ROMANIAN STUDENTS EXPECTATIONS FROM EDUCATIONAL ONLINE PLATFORMS IN THE AGE OF COVID-19

Appears in: INTED2021 Proceedings Publication year: 2021Pages: 10372-10381ISBN: 978-84-09-27666-0ISSN: 2340-1079doi: 10.21125/inted.2021.2162Conference name: 15th International Technology, Education and Development ConferenceDates: 8-9 March, 2021Location: Online Conference
Spatiotemporal Memories for Missing Samples Reconstruction

We develop a systematic theory to reconstruct missing samples in a time series using a spatiotemporal memory based on artificial neural networks. The Markov order of the input process is learned and subsequently used for learning temporal correlations from data difference sequences. We enforce the Lipschitz continuity criterion in our algorithm, leading to a regularized optimization framework for learning. The performance of the algorithm is analyzed using both theory and simulations. The efficacy of the technique is tested on synthetic and real life data sets. Our technique is analytic and uses nonlinear feedback within an optimization setup. Simulation results show that the algorithm presented in this article significantly outperforms the state-of-the-art algorithms for missing samples reconstruction with the same data set and similar training conditions.
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems

The recent many-fold increase in the size of deep neural networks makes efficient distributed training challenging. Many proposals exploit the compressibility of the gradients and propose lossy compression techniques to speed up the communication stage of distributed training. Nevertheless, compression comes at the cost of reduced model quality and extra computation overhead. In this work, we design an efficient compressor with minimal overhead. Noting the sparsity of the gradients, we propose to model the gradients as random variables distributed according to some sparsity-inducing distributions (SIDs). We empirically validate our assumption by studying the statistical characteristics of the evolution of gradient vectors over the training process. We then propose Sparsity-Inducing Distribution-based Compression (SIDCo), a threshold-based sparsification scheme that enjoys similar threshold estimation quality to deep gradient compression (DGC) while being faster by imposing lower compression overhead. Our extensive evaluation of popular machine learning benchmarks involving both recurrent neural network (RNN) and convolution neural network (CNN) models shows that SIDCo speeds up training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression baseline, Topk, and DGC compressors, respectively.
Variance Reduced Methods for Non-convex Composition Optimization

This paper explores the non-convex composition optimization consisting of inner and outer finite-sum functions with a large number of component functions. This problem arises in important applications such as nonlinear embedding and reinforcement learning. Although existing approaches such as stochastic gradient descent (SGD) and stochastic variance reduced gradient (SVRG) descent can be applied to solve this problem, their query complexities tend to be high, especially when the number of inner component functions is large. Therefore, to significantly improve the query complexity of current approaches, we have devised the stochastic composition via variance reduction (SCVR). What's more, we analyze the query complexity under different numbers of inner function and outer function. Based on different kinds of estimation of inner component function, we also present the SCVRII algorithm, though the order of query complexities are the same with SCVR. Additionally, we propose an extension to handle the mini-batch cases, which improve the query complexity under the optimal mini-batch size. The experimental results validate our proposed algorithms and theoretical analyses.
Cross-domain sentiment analysis model on Indonesian YouTube comment

A cross-domain sentiment analysis (CDSA) study in the Indonesian language and tree-based ensemble machine learning is quite interesting. CDSA is useful to support the labeling process of cross-domain sentiment and reduce any dependence on the experts; however, the mechanism in the opinion unstructured by stop word, language expressions, and Indonesian slang words is unidentified yet. This study aimed to obtain the best model of CDSA for the opinion in Indonesia language that commonly is full of stop words and slang words in the Indonesian dialect. This study was purposely to observe the benefits of the stop words cleaning and slang words conversion in CDSA in the Indonesian language form. It was also to find out which machine learning method is suitable for this model. This study started by crawling five datasets of the comments on YouTube from 5 different domains. The dataset was copied into two groups: the dataset group without any process of stop word cleaning and slang word conversion and the dataset group to stop word cleaning and slang word conversion. CDSA model was built for each dataset group and then tested using two types of tree-based ensemble machine learning, i.e., Random Forest (RF) and Extra Tree (ET) classifier, and tested using three types of non-ensemble machine learning, including Naïve Bayes (NB), SVM, and Decision Tree (DT) as the comparison. Then, It can be suggested that the accuracy of CDSA in Indonesia Language increased if it still removed the stop words and converted the slang words. The best classifier model was built using tree-based ensemble machine learning, particularly ET, as in this study, the ET model could achieve the highest accuracy by 91.19%. This model is expected to be the CDSA technique alternative in the Indonesian language.
INFLUENCE OF DIGITAL IMAGES PRELIMINARY NOISING ON STATISTICAL STEGDETECTORS PERFORMANCE

Context. The problem of sensitive information protection during data transmission in communication systems was considered. The case of reliable detection of stego images formed according to advanced embedding methods was investigated. The object of research is digital images steganalysis of adaptive steganographic methods.&#x0D; Objective. The goal of the work is performance analysis of statistical stegdetectors for adaptive embedding methods in case of preliminary noising of analyzed image with thermal and shot noises.&#x0D; Method. The image pre-processing (calibration) method was proposed for improving stego-to-cover ratio for state-of-the-art adaptive embedding methods HUGO, MG and MiPOD. The method is aimed at amplifying negligible changes of cover image caused by message hiding with usage of Gaussian and Poisson noises. The former one is related to influence the thermal noise of chargecoupled device (CCD) based image sensor during data acquisition. The latter one is related to shot noise that originates from stochastic process of electron emission by photons hitting of CCD elements. During the research, parameters of thermal noise were estimated with two-dimensional Wiener filter, while sliding window of size 5·5 pixels was used for parameters evaluation for shot noise.&#x0D; Results. The dependencies of detection error on cover image payload for advance HUGO, MG and MiPOD embedding methods were obtained. The results were presented for the case of image pre-noising with both Gaussian and Poisson noises, and varying of feature pre-processing methods.&#x0D; Conclusions. The conducted experiments confirmed effectiveness of proposed approach for image calibration with Poisson noise. Obtained results allow us to recommend linearly transformed features to be used for improving stegdetector performance by natural image processing. The prospects for further research may include investigation usage of special noises, such as fractal noises, for improving stego-to-cover ratio for advanced embedding methods.
ARGESIM Benchmark C11 'SCARA Robot’ with Extended Trajectory Tracking Control

Modelling of mechatronic systems often results in implicit model description.Simulation systems provide different strategies to deal with this kind of problem, resulting in different modelling approaches.For this purpose, Mathwork's MATLAB system offers in its basic MATLAB system at programming level implicit ODE solvers, and in its toolboxes Simulink and SimMechanics (on basis of the Modelica-like system SimScape) graphical modelling environment.This Benchmark Study compares these modelling approaches and the simulation efficiency and results on basis of the ARGESIM Benchmark C11 'SCARA Robot'.Additionally, the contribution's investigations present a tuning of the PID control for point-to-point movement with and without collision prevention and introduce a trajectory tracking control with collision prevention, which improves the performance essentially.
Unsupervised Feature Selection for Histogram-Valued Symbolic Data Using Hierarchical Conceptual Clustering

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described using a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. Minimizing the compactness is equivalent to maximizing the dis-similarity of the generated cluster, i.e., concept, against the whole concept in each step. In this sense, the compactness plays the role of cluster quality. We also show that the average compactness of each feature with respect to objects and/or clusters in several clustering steps is useful as a feature effectiveness criterion. Features having small average compactness are mutually covariate and are able to detect a geometrically thin structure embedded in the given multi-dimensional histogram-valued data. We obtain thorough understandings of the given data via visualization using dendrograms and scatter diagrams with respect to the selected informative features. We illustrate the effectiveness of the proposed method by using an artificial data set and real histogram-valued data sets.
Model-driven development of augmented reality-based editors for domain specific languages

Designing visual models to describe and conceptualize objects and systems requires abstraction skills and a predisposition for visual interactions. Readily available modeling tools rely on the users’ logical-mathematical and visual-spatial abilities to support modeling design. However, they fall short of mechanisms to tap into the users’ bodily-kinesthetic abilities. This research presents a model-driven framework to automatically develop visual editors to work with Domain Specific Languages in tangible interaction environments. The framework is illustrated through the development of an editor of entity-relationship models supported by augmented reality. The editor usability evaluation indicates good acceptance by users as well as potential to support alternative interactions and to learn database concepts.
Accelerated Adaptive Backstepping Control of the Chaotic MEMS Gyroscope by Using the Type-2 Sequential FNN

In this paper, we propose an accelerated adaptive backstepping control algorithm based on the type2 sequential fuzzy neural network (T2SFNN) for the microelectromechanical system (MEMS) gyroscope with deadzone and constraints. Firstly, the mathematical model of the MEMS gyroscope is established to perform dynamical analyses and controller design. Then, the phase diagrams and Lyapunov exponents are presented to reveal its chaotic oscillation, which is harmful to system stability. In order to suppress oscillations derived from chaos and deadzone, an accelerated adaptive backstepping controller is proposed wherein an adaptive auxiliary is established to compensate the influence of nonsymmetric deadzone on stability performance, along with the T2SFNN designed to approximate unknown functions of dynamic systems. Furthermore, the speed function is introduced to accelerate convergence speed of the control system, and the problem of complex term explosion in traditional backstepping is successfully solved by a secondorder tracking differentiator. Finally, simulation results show that the proposed control scheme can guarantee asymptotic convergence of all signals in the closedloop system, as well as satisfying states constraints and fulfilling the purposes of chaos suppression and accelerated convergence.
(p+, α, t)-Anonymity Technique Against Privacy Attacks

Privacy preservation is a major concern in current technology where enormous amounts of data are being collected and published for carrying out analysis. These data may contain sensitive information related to individual who owns them. If the data is published in their original form, they may lead to privacy disclosure which threats privacy requirements. Hence, the data should be anonymized before publishing so that it becomes challenging for intruders to obtain sensitive information by means of any privacy attack model. There are popular data anonymization techniques such as k-anonymity, l-diversity, p-sensitive k-anonymity, (l, m, d) anonymity, and t-closeness, which are vulnerable to different privacy attacks discussed in this paper. The proposed technique called (p+, α, t)-anonymity aims to anonymize the data in such a way that even though intruder has sufficient background knowledge on the target individual he will not be able to infer anything and breach private information. The anonymized data also provide sufficient data utility by allowing various data analytics to be performed.
A Comparative Analysis for 2D Object Recognition: A Case Study with Tactode Puzzle-Like Tiles

Object recognition represents the ability of a system to identify objects, humans or animals in images. Within this domain, this work presents a comparative analysis among different classification methods aiming at Tactode tile recognition. The covered methods include: (i) machine learning with HOG and SVM; (ii) deep learning with CNNs such as VGG16, VGG19, ResNet152, MobileNetV2, SSD and YOLOv4; (iii) matching of handcrafted features with SIFT, SURF, BRISK and ORB; and (iv) template matching. A dataset was created to train learning-based methods (i and ii), and with respect to the other methods (iii and iv), a template dataset was used. To evaluate the performance of the recognition methods, two test datasets were built: tactode_small and tactode_big, which consisted of 288 and 12,000 images, holding 2784 and 96,000 regions of interest for classification, respectively. SSD and YOLOv4 were the worst methods for their domain, whereas ResNet152 and MobileNetV2 showed that they were strong recognition methods. SURF, ORB and BRISK demonstrated great recognition performance, while SIFT was the worst of this type of method. The methods based on template matching attained reasonable recognition results, falling behind most other methods. The top three methods of this study were: VGG16 with an accuracy of 99.96% and 99.95% for tactode_small and tactode_big, respectively; VGG19 with an accuracy of 99.96% and 99.68% for the same datasets; and HOG and SVM, which reached an accuracy of 99.93% for tactode_small and 99.86% for tactode_big, while at the same time presenting average execution times of 0.323 s and 0.232 s on the respective datasets, being the fastest method overall. This work demonstrated that VGG16 was the best choice for this case study, since it minimised the misclassifications for both test datasets.
Dialogical Argumentation and Textual Entailment

In this chapter, we introduce a new dialogical system for first order classical logic which is close to natural language argumentation, and we prove its completeness with respect to usual classical validity. We combine our dialogical system with the Grail syntactic and semantic parser developed by the second author in order to address automated textual entailment, that is, we use it for deciding whether or not a sentence is a consequence of a short text. This work—which connects natural language semantics and argumentation with dialogical logic—can be viewed as a step towards an inferentialist view of natural language semantics.
Geochemical Study Geochemical Study of Injana Formation and its Tectonic Significances in Selected Areas-Iraq

This study is concerned with geochemical analysis of major oxides; these are SiO2, CaO, MgO, Fe2O3, Al2O3, Na2O, K2O, TiO2, in addition to MnO. Atomic Absorption Spectrophotometer and Ultra Violet Spectrophotometer tools were used to examine ten samples represented the detritus LOI sediments of Injana Formation in Iraq in two sites which are (S1) site at Dohuk Governorate and (S2) at Al-Najaf Governorate; where five samples for each sites were collected.