Reviewerly/exHarmony huggingface dataset:
Software Test Management to Improve Software Product Quality

Improving software quality is one of the desired goals of software development teams. More so, financial companies must ensure the quality of the software product to guarantee financial transactions. For this reason, this study, based on the Design Science Research (DSR) approach, establishes the research question: Does implementing a test management framework in the development process improve the quality of the software product? The paper aims to answer this question we propose a software testing framework based on the processes described in the ISO/IEC/IEEE 29119–2 standard and the documentation templates of the ISO/IEC/IEEE 29119–3 standard, which became the technical guide for the evaluation of the developed software. Furthermore, we evaluated the framework through a case study applied in a financial company in Ecuador; we also publish the testing framework artifacts in a Zenodo open data repository. The principal results show an increase in bug detection in the range of 77% to 100%, reduction of defect density in the range of 95% to 0%, and a 12.5% reduction of previously reported software failures in production environments.
Correction to: Music Analysis with Phase Portraits

The original version of the book was inadvertently published with an incorrect equation ( 13.1 ) on page 364 in chapter 13 .
CIS Publication Spotlight [Publication Spotlight]

“For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.”
Underwater Image Registration Based on Polar Complex Exponential Transform

In order to overcome the shortcomings of insufficient operation accuracy and sensitivity in the operation of underwater machines, it is necessary to improve the positioning accuracy of the multi-camera for the underwater robots, which requires an accurate and rapid registration method for underwater images. In this paper, a novel underwater image registration method is proposed by using SURF (Speeded Up Robust Features) feature detection and PCET (Polar Complex Exponential Transform) moments for feature description and matching with fast calculation speed and better registration accuracy. SURF feature detection algorithm is used to extract the feature points firstly. Then, the region descriptor is constructed using PCET moments to represent the local feature of image patch for rough matching. Finally, the RANSAC (RANdom SAmple Consensus) algorithm is applied to remove mismatches for obtaining the correct transformation parameters. The experimental results confirm that the proposed method can achieve an accurate registration for underwater images with higher speed and better performance than other classical methods.
Design and Application of Endangered Animal Monitoring System Based on Mobile APP

In view of the wide distribution area of Yangtze finless porpoise, the small number of existing population and the insufficient number of professional monitors, a mobile app-based finless porpoise information collection system is proposed and designed. The app obtains the number, status and location information of finless porpoise by collecting photos or short videos uploaded by social workers in each river section, and utilizes them through management processing. The paper describes the design scheme of the system and introduces the module functions of the program such as photo uploading, background information confirmation, and porpoise news pushing. The adoption of mobile app makes the system “easy to install and refundable”, which facilitates more people to participate in finless porpoise conservation.
Automatic Establishment of Generic Relationships Between Concepts

The article describes the results of research on the creation of methods for constructing a formalized semantic description of documents for solving problems of semantic search for documents. The methods used in the study are based on the use of machine grammar procedures and conceptual analysis of texts, which ensure the identification of its conceptual composition and the assignment of characteristics to the names of concepts that correspond to their semantic role and significance in the text. To perform this work, a set of software tools was created, which was tested on media documents.
ACADEMIC CLASSES: CLUSTER INTEGRATION OF SOCIAL PARTNERS

The article clarifies and concretizes the postulates of project management in the format of a general education organization. The main positions of the strategic vision of academic classes, characteristics of the content and organization of the technological profile of training with an engineering focus are presented. Special attention is paid to the creation and development of a territorial and multi-sectoral educational and career guidance cluster.
COMPARATIVE ANALYSIS OF BIO-INSPIRED OPTIMIZATION ALGORITHMS IN NEURAL NETWORK BASED DATA MINING CLASSIFICATION

It always helps to determine optimal solutions for stochastic problems thereby maintaining good balance between its key elements. Nature inspired algorithms are meta-heuristics that mimic the natural activities for solving optimization issues in the era of computation. In the past decades, several research works have been presented for optimization especially in the field of data mining. This paper addresses the implementation of bio-inspired optimization techniques for machine learning based data mining classification by four different optimization algorithms. The stochastic problems are overcome by training the neural network model with techniques such as barnacles mating , black widow optimization, cuckoo algorithm and elephant herd optimization. The experiments are performed on five different datasets, and the outcomes are compared with existing methods with respect to runtime, mean square error and classification rate. From the experimental analysis, the proposed bio-inspired optimization algorithms are found to be effective for classification with neural network training.
Implicit Opinion Aspect Clues in Portuguese Texts: Analysis and Categorization

Although very useful, aspect-based sentiment analysis shows several challenges. The occurrence of implicit aspects is one of them. To improve our understanding of implicit aspects, this work analyzes their composition and categorizes the implicit aspect clues found in two corpora of opinionated texts of different domains in the Portuguese language. As results of this work, in addition to the implicit aspect typology, we present four lexicons of implicit aspect clues for different domains and the annotation of implicit aspect clues in an existing corpus.
Power Business Management Platform Based on Blockchain and Trusted Computing

With the continuous expansion of the scale of power grids and the increasing number of power users, the demand for data credibility, software reliability, and failure recovery capabilities in the power business is increasing. Trusted computing and blockchain, as emerging technologies in information security protection, can guarantee a safe and reliable operating environment and management mechanism in the power Internet of Things. Based on this, a power business management platform that integrates blockchain and trusted computing is proposed. First of all, use trusted computing to provide a guarantee for the operating environment of the blockchain and the storage and transmission of data in sure of the safe operation of the blockchain and the synergy of data on and off the blockchain; use the blockchain to record data information, transaction information, and business process records, which can help the safe operation of power business and the traceability of business data. In this way, we build a credible power business management platform.
CLASSIFICATION OF BAOULE SENTENCES ACCORDING TO FREQUENCY AND SEGMENTATION OF TERMS VIA CONVOLUTIONAL NEURAL NETWORKS

In the Baoule language, several sentences express the same fact. Classification of sentences is a task of Natural Language Processing (NLP). Deep learning has turned out to be a kind of method that has a significant effect in this area. In this paper, we propose a convolutional neural network (CNN) based system for sentence classification. We introduce into this system a word representation model to capture semantic characteristics by encoding the frequency of terms and segmenting the sentence into clauses. The experimental results show that our system produces satisfactory results.
STUDENT INDEPENDENT WORK MANAGEMENT TECHNOLOGY BASED ON QUALITY MANAGEMENT SYSTEM PRINCIPLES

The article considers the technology of managing the independent work of students at the university based on the process approach. The essence of the principles of the quality management system is revealed, in comparison with the concepts, principles, approaches to managing the educational process existing in pedagogical science, the components of the process of managing the independent work of students are identified and described, the decomposition of this process is given with specification for each subprocess of evaluation criteria.
Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation

Quantum chemistry and materials is one of the most promising applications of quantum computing. Yet much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them. Most previous efforts have carried out resource estimations for quantum algorithms run on large-scale fault-tolerant architectures, which include the quantum phase estimation algorithm. In contrast, few have assessed the performance of near-term quantum algorithms, which include the variational quantum eigensolver (VQE) algorithm. Recently, a large-scale benchmark study [Gonthier et al. 2020] found evidence that the performance of the variational quantum eigensolver for a set of industry-relevant molecules may be too inefficient to be of practical use. This motivates the need for developing and assessing methods that improve the efficiency of VQE. In this work, we predict the runtime of the energy estimation subroutine of VQE when using robust amplitude estimation (RAE) to estimate Pauli expectation values. Under conservative assumptions, our resource estimation predicts that RAE can reduce the runtime over the standard estimation method in VQE by one to two orders of magnitude. Despite this improvement, we find that the runtimes are still too large to be practical. These findings motivate two complementary efforts towards quantum advantage: 1) the investigation of more efficient near-term methods for ground state energy estimation and 2) the development of problem instances that are of industrial value and classically challenging, but better suited to quantum computation.
Multitask Deep Learning Model with Efficient Encoding Layer and Enhanced Parallel Convolution Block

In recent years, multitask learning has turned out to be of great success in various applications. Though single task model training has promised great results throughout these years, it ignores valuable information which might help in estimating better learning parameters. By virtue of learning-related tasks, multitask learning has been able to generalize the models even better. In this paper, we try to enhance the feature mapping of multitask deep learning models by sharing features among related tasks and inductive transfer learning. We have explored the learning of task relationship among various tasks for acquiring more benefits from multitask learning. The proposed enhanced model is compared with two state-of-the-art multitask deep learning models, namely Hyperface and FaceNet. The results show better performance of the proposed model in predicting age, gender and ethnicity on the UTK face dataset.
Research and Analysis of Computer Multimedia Technology in Basketball Tactics Teaching

With the development of the new curriculum reform, multimedia technology has been widely used in all aspects of modern teaching, which can be well applied in the existing teaching mode. This paper mainly discusses the computer multimedia technology aided basketball tactical teaching design, with the help of various teaching means, improve the basketball tactical teaching effect, change the teaching method, and integrate the traditional teaching method with the multimedia assisted teaching.
Research on Small Aerial Target Detection Based on Salient Region

Target detection algorithm based on deep learning has become a hotspot with its practicability and adaptability. However, due to the small scale of small targets and lack of feature information, it is weak to effectively detect small targets in air by deep learning algorithm. To solve this problem, this paper proposes a novel detection scheme for small aerial targets based on salient region. This detection scheme combines a haze removal algorithm, a skyline detection algorithm and a Frequency-Tuned (FT) salient region detection algorithm to detect small targets in air. Through the usability of the haze removal algorithm, a relatively clear image can be obtained for subsequent detection processing. Next, by applying the skyline detection algorithm to the image obtained in the previous step, the sky background region with simple pixel level features can be obtained, which is free from the interference of complex ground features on the aerial target detection. Finally, the Frequency-Tuned salient region detection algorithm is used to detect the significant pixel area in the sky background to obtain the target position. Experimental results demonstrate that the detection scheme proposed in this paper can effectively detect small aerial targets.
FELICIDADE NO TRABALHO: MAPEAMENTO DA PRODUÇÃO CIENTÍFICA DA ÚLTIMA DÉCADA POR MEIO DE ANÁLISE BIBLIOMÉTRICA

The aim of this article was to investigate the configuration of scientific production on job happiness, taking as source the main international databases, the ISI Web of Science (WOS) and Scopus, in the 2011-2020 timeframe. From a methodological point of view, the study was developed following the laws of Bradford, Lotka and Zipf in search of the main authors and their productivity, related topics covered and their evolution in the period 2011-2020. Data were processed using R and Excel® software. The study results allow us to state that happiness at work is being considered as part of the well-being construct in the main publications on the subject. However, it is necessary to consider happiness “in the organizational environment”, not just “at work”, appropriating the term happiness and developing a new construct of the concept.
An MPF Based Aerial Platform Tracking Method for Road Network Targets

Abstract This paper presents a new algorithm to improve the tracking accuracy of the aerial mobile observation platform by using road information constraints. Combined with the road information and Mixed Particle Filter(MPF) algorithm, the Road Constrained Mixed Particle Filter(RCMPF) algorithm is proposed. RCMPF algorithm can effectively solve the problem of non-linear observation equation of aerial observation platform and realize the tracking of ground targets. The simulation results show that the algorithm improves the tracking accuracy of the target.
Modelling Eye-Gaze Movement Using Gaussian Auto-regression Hidden Markov

Modelling and prediction of eye gaze movement can be highly desirable in many real-world scenarios, e.g. human-machine interaction and human behavior analysis. This challenging area largely remains unexplored. In this study we tackle this challenge and propose a method to predict eye-gaze movement of human observers. Eye gaze trajectories are separated into three components, where two of them are considered as noise or bias, which can be removed from the trajectory data. So the remaining component, principle movement, can be modelled by a proposed new method, GAR HMM, which stands for Gaussian Auto-regression Hidden Markov Model based on AR HMM. Instead of the Beta Processes in AR HMM, GAR HMM introduces a Gaussian Process. So the model can predict the probability of occurrence of eye gaze in each region over time. By joining the predicted points together as a sequence, we can generate the eye gaze movement prediction as a time series. To evaluate GAR HMM we collected eye gaze movement data from over 20 volunteers. Experiments show that good prediction can be achieved by our proposed GAR HMM method. As a groundbreaking work GAR HMM can lead to much further extension to benefit real applications .
Аналіз сучасних досягнень програмної інженерії для створення універсального алгоритму ASCII-ART конвертації

The paper considers the advantages of using ASCII-art technologies. The wide availability and popularity of textual communication channels encourages the use of ASCII-art to represent images. Modern approaches to creating a drawing in the ASCII-art style are analyzed. In the course of the study, various ways of creating ASCII drawings using computer programs were shown. Efficient modern algorithms for converting images into ASCII format have been identified. A perfect algorithm for converting an arbitrary image into ASCII format is proposed, which allows you to evaluate all the advantages of ASCII-art, such as small size of the picture, universality, internationality. Designed and developed a perfect and convenient mobile application that allows you to download the target image from the internal storage or instant photo from the camera and optimizes the process of converting images to ASCII-art. The designed system is a software application that provides more advanced functionality than existing analogues and is an independent product that is not based on any existing systems. To create a software application, a statically typed object-oriented programming language Kotlin was used, and AndroidStudio was chosen as an integrated development environment. The application is intended for sole use. The algorithm for converting images into an ASCII format consists of the following steps: preparing an image for processing, which makes it possible to eliminate incorrect image scaling in the ratio itself and image processing. Experiments were carried out on the independent creation of ASCII-art drawings. The obtained results indicate that the creation of modules for converting ASCII art images will reduce the amount of information stored on servers, attract new users by following the youth direction along with global trends and popularize ASCII graphics among already registered users. The use of the developed application will allow creative individuals to expand the ways of self-realization with the help of modern technologies, overcoming the limitations caused by the onset of the global COVID-19 pandemic. Drawings with symbols are a universal means of communication designed to connect humanity, overcome the barriers of natural languages and state borders.
Text Mining

Text mining is the process of extracting meaning from unstructured text documents using both machine learning and natural language processing techniques. It enables the process of converting reviews into specific recommendations that can be used. Text data would be represented in structured formats through converting text into numerical representations. Structured text data can then be ingested by Artificial Intelligence algorithms for various tasks such as sentence topic classification and keyword extraction.
On the Inadequacy of Multimodal Maps for Cryptographic Hash Applications with Phase-Space Modulation

In this paper, we outline a specific problem concerning the use of multimodal chaotic maps in the core compression-construction of cryptographic hash functions. In cryptographic hash applications, the security level is measured according to their collision resistance property. This paper analyzes the strength of this property, by showing how it could be affected in the case where message units are modulated directly into the chaotic phase space of a multimodal map. Mathematical properties of multimodal maps are discussed and used to prove the risk of collision existence in such hash schemes. Four examples of widely used chaotic-maps in hash constructions are given and analyzed. Finally, we demonstrate the risk of collision existence using a practical example inherited from a previously proposed hash function.
Modeling Uncertainty in Neural Relation Extraction

Previous work on neural relation extraction mainly focus on point-based methods and ignores uncertainty within the bag, thus making poor predictions when there are insufficient instances of the bag. To solve this problem, in this paper, we propose two density-based methods. Specifically, we assume each bag is a Gaussian distribution and sentences in the bag are drawn from it. We use predicted variance, capturing bag’s uncertainty, as well as predicted mean to draw more samples to enrich one-instance bags. We also use predicted variance to vote for good representation and temper the loss. To the best of our knowledge, this is the first paper to model uncertainty in neural relation extraction. Experiment results on NYT-10 show significant improvements over baselines.
2D Topological Map Building by UAVs for Ground Robot Navigation

In air-ground cooperation, unmanned aerial vehicles (UAVs) are used to build a priori map of the ground environment from an aerial perspective, which is conducive to improve the navigation ability of ground robots. This paper proposes a 2D topology map building method from the air view, which can be effectively used for global path planning of ground mobile robots. To realize the 3D reconstruction of the ground from the air view, the 3D Euclidean Signed Distance Field (ESDF) is constructed incrementally from the Truncated Signed Distance Field (TSDF). The ESDF map occupies a large storage space, which is inconvenient to transmit to ground robots. To solve this problem, a lighter topology map is constructed on the basis of the ESDF map. Since ground robots can only move in the 2D plane, the 2D topological map is generated at any height from the ground to represent the topological structure of the ground robots working environment. The experimental results on the dataset and simulation environment shows the effectiveness of the proposed method.