Reviewerly/exHarmony huggingface dataset:
Improving Business Product Owner Commitment in Student Scrum Projects

Aim/Purpose: The purpose of this study was to explore and test several improvements for incorporating the Business Product Owner (BPO) role in student software projects using Scrum. Background: As the popularity of Scrum has grown, its roles and ceremonies have been utilized in student software projects, yet one of the more challenging roles to implement in the academic context is the role of BPO. Consequently, although the BPO role is central to Scrum, this role is lacking in many student Scrum projects. Methodology: Both quantitative and qualitative data were collected at a large university in the U.S. to assess the effectiveness of attempted improvements in incorporating the BPO role in 32 student Scrum projects. Contribution: This study is among the first to theorize and examine the bottom-up selection of BPOs where team members select their own BPOs as a unique project requirement. This study extends current theory to expand the development team roles when BPOs are unable to articulate requirements during co-creation activities. This study also informs us of the impact of BPO attributes on team member perceptions and it demonstrates how BPO engagement can be improved in student Scrum projects. Findings: The findings suggest that BPO engagement can be improved by (a) formalizing the BPO selection process through time-limiting responses, screening, and contractual agreements, (b) starting the BPO initiation process through co-creation activities (e.g., user story workshop), and (c) expanding the development team’s role in defining and prioritizing features during the BPO execution process through mutual steering. Recommendations for Practitioners: Based on the findings, practitioners are recommended to educate BPO’s concerning their role and its impact on the Scrum team. Recommendation for Researchers: Researchers are recommended to develop and test additional mechanisms to facilitate ongoing BPO engagement. Impact on Society: Higher education needs to explore more ways of involving practitioners in student projects. Future Research: Future research should explore ways of reducing BPO attrition in student Scrum projects.
UPCLASS: a deep learning-based classifier for UniProtKB entry publications

Abstract In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliographies in UniProt, we investigate a convolutional neural network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge of categorizing publications at the accession annotation level is that the same publication can be annotated with multiple proteins and thus be associated with different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achieved a micro F1-score of 0.72 and a macro F1-score of 0.62, outperforming baseline models based on logistic regression and support vector machine by up to 22 and 18 percentage points, respectively. We believe that such an approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators to decide whether a publication is relevant for further curation for a protein accession. Database URL: https://goldorak.hesge.ch/bioexpclass/upclass/.
Context-Aware Latent Dirichlet Allocation for Topic Segmentation

We propose a new generative model for topic segmentation based on Latent Dirichlet Allocation. The task is to divide a document into a sequence of topically coherent segments, while preserving long topic change-points (coherency) and keeping short topic segments from getting merged (saliency). Most of the existing models either fuse topic segments by keywords or focus on modeling word co-occurrence patterns without merging. They can hardly achieve both coherency and saliency since many words have high uncertainties in topic assignments due to their polysemous nature. To solve this problem, we introduce topic-specific co-occurrence of word pairs within contexts in modeling, to generate more coherent segments and alleviate the influence of irrelevant words on topic assignment. We also design an optimization algorithm to eliminate redundant items in the generated topic segments. Experimental results show that our proposal produces significant improvements in both topic coherence and topic segmentation.
Feature Based Sentiment Analysis of Mobile Product Reviews using Machine Learning Techniques

With the advent of Web technology and its rapid growth led many people to express their views or opinions on the web related to the products, the services, the events or any social issues.Due to the substantial extent of data which is increasing day by day on the web, it is obvious that the reviews provided by the people on the e-commerce sites is also huge and such reviews are in unstructured form.Hence, it is challenging for the online users, customers and manufacturers to make a proper decision about opinions on these reviews.During the last two decades, many researchers including corporation are working consistently on sentiment analysis, also recognized as, opinion analysis or mining, to derive the opinions expressed by various end users as reviews.In this paper, we study the fine-grained investigation in identifying the sentiments (or opinions) expressed on various aspects of the entity considering the aspects as explicit one over various brands of mobile product reviews and classify these opinions based on some machine learning algorithms.Exploratory outcomes on the different datasets manifest the promising results with respect to the accuracy of classifying the opinions.
Local Heterogeneous Features for Person Re-Identification in Harsh Environments

Local features could learn semantic information for pedestrian images and they are very important for person re-identification (Re-ID) in harsh environments. However, most approaches only optimize one kind of local feature, which results in incomplete local features. In this paper, we propose Local Heterogeneous Features (LHF) to extract discriminative local features from three aspects. To this end, we utilize three kinds of losses to learn three kinds of local features, i.e., local discriminative features, local relative features, local compact features. As for local discriminative features, we split the attention maps into three horizontal sub-regions and perform the classification operation. Then, we divide the attention maps into two horizontal sub-regions, and we synchronously apply the triplet loss and center loss to learn local relative features and local compact features. Finally, we utilize local discriminative features to represent pedestrian. We evaluate LHF on public person Re-ID datasets and prove LHF is meaningful for local feature learning.
Graph-based boosting algorithm to learn labeled and unlabeled data

Ensemble learning is an effective technique to learn the information of data by combining multiple models. But usually the combined models are supervised learning algorithms which need a lot of labeled data to tune their parameters. Some ensemble learning algorithms were proposed to exploit the information of unlabeled data. These methods had to learn the samples with pseudo-labels due to the scarcity of labeled data. But it's inevitable for the samples with pseudo-labels to bring wrong information during training process. In this paper, we will propose a novel graph-based boosting (GBB) algorithm to learn labeled and unlabeled data. GBB is a framework combining many models linearly. And pseudo-labels will not occur during training process. GBB will assign a new weighting vector for the labeled samples and a transformed similarity matrix for all samples to train the combined model at each iteration. We also extend GBB, termed as weighted GBB (WGBB), to learn imbalanced data by adding a weighting vector for the labeled data. Finally, 14 relatively balanced datasets and 22 imbalanced datasets are used to validate the performances of GBB and WGBB respectively. Experimental results illustrate that GBB can achieve a competitive performance and WGBB has an obvious advantage to handle classification problem of imbalanced data, comparing with other related algorithms.
An Efficient and Rapid Method for Detection of Mutations in Deoxyribonucleic Acid - Sequences

The comparison of genomic sequences plays a key role in determining the structural and functional relationships between genes.This comparison is carried out by identifying the similarities, differences and mutations between genomic sequences.This makes it possible to study and analyze the genetic and the evolutionary relationships between organisms.Alignment algorithms have been in the spotlight for the last few decades, due to a vast genomic data explosion.They have attracted a great deal of interest from many researchers who focus on the development of practical solutions to ensure effective alignments with an optimal response time.In this paper, a novel algorithm based on Discrete To Continuous "DTC" approach has been developed.The proposed methodology was compared against other existing methods, which are largely based on the concept of string matching.Experimental results show that the DTC algorithm delivers supremely efficient alignment with a reduced response time.
Using mobile augmented reality to enhancing students’ conceptual understanding of physically-based rendering in 3D animation

Physically-based rendering (PBR) concept is widely use nowadays in 3D rendering works, this concepts interested in the ray of light for describes the interaction of light and materials. Understanding the principles of PBR can be easier to adjust shader parameters to be realistic, react correctly to changes in lighting condition and also giving the same results even in different 3D rendering software. In Shading lighting and rendering (SLR) subject which PBR concepts become important concept instead the 'old style rendering' it was found that students are unable to clearly understand concepts of PBR. According to the problem, in this research bringing mobile augmented reality (AR) to be a media for demonstrate PBR concepts for students. The sample were 35 students from Department of Animation and Digital Media, Bansomdejchaopraya Rajabhat University. The results found that students understand the PBR concepts and can adjust the PBR shader paremeters to be realistic assess from students pre-test, post-test scores and students homework. The mobile AR application media were usable for students and suitable to be a learning media in 3D animation assess form students' response.
Enrich cross-lingual entity links for online wikis via multi-modal semantic matching

The task of enriching cross-lingual links is to find articles in different languages but representing the same real-world object between multilingual Wikis. In this paper, we propose a novel Multi-Modal Semantic Matching approach, called MMSM, to enrich cross-lingual links for online Wikis. Specifically, MMSM jointly trains two novel end-to-end neural matching models, Entity Description Matching Model and Entity Image Matching Model, which can utilize entity description and images for the cross-lingual entity matching. To the best of our knowledge, it is the first work to utilize multi-modal information to enrich cross-lingual entity links. In the experiments on three datasets CEMZH−ENEasy, CEMZH−ENChallenge and CEMFR−ENEasy, our approach gets the best performance compared with other baseline approaches.
Real-time facial pose estimation and tracking by coarse-to-fine iterative optimization

We present a novel and efficient method for real-time multiple facial poses estimation and tracking in a single frame or video. First, we combine two standard convolutional neural network models for face detection and mean shape learning to generate initial estimations of alignment and pose. Then, we design a bi-objective optimization strategy to iteratively refine the obtained estimations. This strategy achieves faster speed and more accurate outputs. Finally, we further apply algebraic filtering processing, including Gaussian filter for background removal and extended Kalman filter for target prediction, to maintain real-time tracking superiority. Only general RGB photos or videos are required, which are captured by a commodity monocular camera without any priori or label. We demonstrate the advantages of our approach by comparing it with the most recent work in terms of performance and accuracy.
Secure and efficient quantum designated verifier signature scheme

A digital signature with designated verifier (SWDV) makes that only the designated verifier can verify its validity. It can be used to protect the privacy and economic interest of the signer. So, the SWDV schemes have many applications in e-voting, auction and some other fields. To make the SWDV secure against the quantum forger, a quantum signature with designated verifier signature (QSWDV) scheme is proposed. Our QSWDV is robust due to its strong security against forgery attack, inter-resending attacks, impersonation attacks and Trojan horse attacks. Its properties such as non-transferability and hiding source can be guaranteed by the signature simulation of the designated verifier. What is more, our scheme can be proved to be information-theoretically secure, which can guarantee the secrecy of the signer’s private key and the unforgeability of the QSWDV. In the proposed QSWDV scheme, the partners neither need use quantum one-way function nor need perform quantum state comparisons, which can reduce the complexity and improve the efficiency the QSWDV scheme.
MODELS OF FOREIGN LANGUAGE PRIMARY SCHOOL TEACHER TRAINING IN THE EU

The monograph presents European and regional development trends in innovative scientific researches in pedagogical sciences. The general issues of the history of pedagogy, theory and methodology of teaching, correctional pedagogy, theory and methodology of vocational education and management of education, information and communication technologies in education, etc. are considered. The publication is intended for scholars, teachers, postgraduate students, and students of pedagogical specialties, as well as a wide readership interested in pedagogy. Innovative scientific researches: European development trends and regional aspect. Collective monograph. – 1st ed. – Riga, Latvia : “Baltija Publishing”, 2020. – 492 p.
DECISION SUPPORT TECHNOLOGY FOR SPRINT PLANNING

Context. The article describes the relevant planning process of software projects, planning problems and different solutions to these problems basis on use of the Scrum methodology.Objective. The purpose of the work is to develop the technology for solving the sprint planning task in the face of uncertainty and possible risks from software development standpoint.Method. The most used software life cycle models are described. The choice of the Scrum as a widely used representative of agile methodology for software development is justified. An analytical review of the methods for estimation of the complexity of user stories is carried out. The major problems of sprint planning are highlighted. The model of the business process to implement an ITproject by Scrum in the form of an BPMN-diagram has been developed. The algorithm to solve the problem of Sprint Backlog planning with uncertainty has been elaborated. The common process of user stories selection from Product Backlog to Sprint Backlog and ways of solving the possible problems are considered. The task of estimation of labor intensity of user stories and the task of risk evaluation in planning are formalized. The technology of user story selection for Sprint Backlog has been developed. Numerical studies of the decision support technology proposed in the article are carried out. It allows suggesting it as the practical tool during sprint planning. The method of adequacy evaluation of proposed technology is offered. The set of key performance indicators for assessing the team performance is selected. Results. The sprint planning technology was developed, which project managers, product owners and development teams for increasing the effectiveness of decision-making process can use. Conclusions. The conducted experiments have confirmed the importance of the proposed decision support technology and allow recommending it for use in practice for planning of software projects. Scientific novelty is to improve the sprint planning process with the assistance of the proposed technology, which alleviates uncertainty while defining labor intensity of user stories and decreases time spent on decision making.
Evaluation of Onychomycosis Information on the Internet

Onychomycosis is a common and significant nail condition causing both physical and social impairment. Since patients often search for health information online, the accuracy of this information has become important. In this study, we sought to assess the reliability and comprehensibility of accessible internet information for patients searching for onychomycosis. We identified the top search engine hits, evaluating websites on several categories: Accountability, Quality of Medical Information, Readability, Display, Support Features, and Transparency/Disclosures. Utilizing a pro forma based on established internet codes of conduct, website readability scores, and peer-reviewed papers, we objectively analyzed and scored the most commonly-listed websites on onychomycosis. Fifty-one total websites were reviewed with a maximum possible overall score of 43. The mean overall score for all websites was 20 and 1/43 (range, 4-35) with varied Accountability (mean, 4.9/10; range, 0-10) and Quality (mean, 6.4/13; range,1-12/13). Readability was poor overall with only 1/3 of sites meeting the acceptable 7th grade reading level for patients. In addition, while sites such as the American Academy of Dermatology website were well-organized and highly readable (Readability score, 5/5), this may compromise the quality of medical information presented (Quality score, 6/13). Because online education materials set the expectations and concerns of patients with onychomycosis, the variability in website reliability necessitates more efficient and regulated methods of presenting health information.
Optimization of Lennard-Jones clusters by particle swarm optimization with quasi-physical strategy

The goal of Lennard-Jones (LJ) clusters optimization is to find the minimum value of the potential function of a cluster and thereby determine the stable configuration of the cluster. It is essentially a completely inseparable multimodal global optimization problem, and using the traditional particle swarm algorithm to solve it often results in local convergence, which means that the solution accuracy of the algorithm is not high. Thus, in this study, we develop a LJ algorithm using a particle swarm optimization (PSO) method and a physical approach to improve the solution accuracy. In this quasi-physical strategy (QPS), the particle swarm algorithm is used to simulate the real atomic structure and incorporates the interatomic force to construct a convergence model so that the algorithm performs well in both global and local space. The potential energy functions of a variety of LJ cluster systems are selected as test functions, and the improved PSO algorithm (QPS-PSO) is analyzed and compared with a competitive swarm optimizer, cooperative coevolution PSO, and differential-group cooperative coevolution, variable-length PSO for feature selection, heterogeneous comprehensive learning PSO, ensemble PSO and cooperative coevolution with differential optimization. The results show that the PSO algorithm for LJ clusters using the proposed QPS has noticeably superior solution accuracy, especially in high-dimensional spaces.
YOLOv3-darknet with Adaptive Clustering Anchor Box for Garbage Detection in Intelligent Sanitation

In recent years, as people’s requirements for living environment are getting higher and higher, many countries have written environmental protection into relevant laws. How to find and clean up garbage in a timely manner is particularly important. Manually searching garbage is not only time consuming, but also requires a lot of manpower. Deep learning has achieved good results in the fields of object detection and semantic segmentation with a good ability to identify these uncertain features just like garbage. In this paper, we capture the garbage image on the road in real time by sanitation vehicle. The YOLOv3-darknet model which is based on adaptive clustering anchor box are used for garbage detection. Multi-category confrontation training is performed on objects that are frequently misdetected. Combined with other auxiliary ranging devices, the latitude and longitude information of the garbage is determined, so that the garbage can be cleaned with a target. Experiments show that our model has achieved good results in garbage detection. The detection time of a single image on the GTX1080ti is 60ms.
Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors

The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.