Reviewerly/exHarmony huggingface dataset:
Empirical Findings on BDD Story Parsing to Support Consistency Assurance between Requirements and Artifacts

Behaviour-Driven Development (BDD) stories have gained considerable attention in recent years as an effective way to specify and test user requirements in agile software development projects. External testing frameworks also allow developers to automate the execution of BDD stories and check whether a fully functional software system behaves as expected. However, other software artifacts may quite often lose synchronization with the stories, and many inconsistencies can arise with respect to requirements representation. This paper reports on preliminary empirical findings regarding the performance of two existing approaches in the literature intended to support consistency assurance between BDD stories and software artifacts. The first approach involves the parsing of BDD stories in order to identify conceptual elements to automatically generate consistent class diagrams, while the second approach seeks to identify interaction elements to automatically assess the consistency of task models and GUI prototypes. We report on the precision of these approaches when applied to a study with BDD stories previously written by Product Owners (POs). Based on the results, we also identify a set of challenges and opportunities for BDD stories in the consistency assurance of such artifacts.
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts are affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-Net, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it should emphasize learning from COVID-related signals and when it should learn from the historical model. Thus, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.
Human Evolution: the Limits of Technocentrism

The purpose of this article is to define the limits of technocentrism through the analysis of the limiting opportunities of technique and technology from certain value positions. Theoretical basis. The philosophical anthropology of Helmut Plessner (the axiological direction in anthropology and neo-institutionalism) was the research methodology. Originality. The institutional use of technology gives it the character of a social phenomenon and turns it into technology. The ability of individuals, which is aimed at achieving a certain goal with the help of certain sustainable techniques, is not yet technology in itself but is only a certain author’s technique. Such subjectively acquired technique can be turned into socially used technology, otherwise, it will be lost. Technology is a technique that has gained recognition and has been mastered by those who did not invent it but used the algorithm proposed by the inventor, a detailed and functionally sound explanation, a method of constructing this technique. But the main thing is that technology is a technique that has received an acceptable justification for society. Conclusions. Technology is not only a means of achieving the goal, it is a way for a human being to transform the world. As such, technology is a component of human himself/herself and changes human – more precisely, a human being changed himself/herself with the help of technologies that he/she creates. However, this creates certain limits of such transformations: technology cannot replace humans in their ability to self-reproduce. Technology is always an element of social communication: the success of communication is interdependent on the success of the technology. Social modernization includes new technologies, but a more important component of social modernization is the new values for which these new technologies are created. Human evolution generates the technocratism at a certain stage. But to the extent that technocracy begins to contradict the values of humans and society, it loses its source of development – human creativity.
A New Validity Index for Fuzzy-Possibilistic C-Means Clustering

In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining the optimal number of clusters. This paper presents a new validity index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic)FP (index, which works well in the presence of clusters that vary in shape and density. Moreover, FPCM like most of the clustering algorithms is susceptible to some initial parameters. In this regard, in addition to the number of clusters, FPCM requires a priori selection of the degree of fuzziness (m) and the degree of typicality (η). Therefore, we presented an efficient procedure for determining an optimal value for and . The proposed approach has been evaluated using several synthetic and real-world datasets. Final computational results demonstrate the capabilities and reliability of the proposed approach compared with several well-known fuzzy validity indices in the literature. Furthermore, to clarify the ability of the proposed method in real applications, the proposed method is implemented in microarray gene expression data clustering and medical image segmentation.
Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentative structure.A significant obstacle to research in this area for the Russian language is the lack of annotated Russian-language text corpora.This article explores the possibility of improving the quality of argumentation mining using the extension of the Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based on the machine translation of the Persuasive Essays Corpus (PersEssays).To make it possible to use these two corpora combined, we propose a Joint Argument Annotation Scheme based on the schemes used in ArgMicro and PersEssays.We solve the problem of classifying argumentative discourse units (ADUs) into two classes -"pro" ("for") and "opp" ("against") using traditional machine learning techniques (SVM, Bagging and XGBoost) and a deep neural network (BERT model).An ensemble of XGBoost and BERT models was proposed, which showed the highest performance of ADUs classification for both corpora.
DIAGNOSIS OF BREAST CANCER FROM X-RAY IMAGES USING DEEP LEARNING METHODS

Ponte Academic JournalJun 2021, Volume 77, Issue 6 DIAGNOSIS OF BREAST CANCER FROM X-RAY IMAGES USING DEEP LEARNING METHODSAuthor(s): Peren Jerfi Canatalay ,Osman Nuri Uçan, Metin ZontulJ. Ponte - Jun 2021 - Volume 77 - Issue 6 doi: 10.21506/j.ponte.2021.6.1 Abstract:In developing and developed nations, the risk of getting breast cancer is among the highest for women. An early diagnosis of breast cancer allows patients to be treated before metastasis (i. to I diagnoses and classi?es), detects and classi?es. This article proposed three conventional methods based on a different dataset from the rest in the literature. The models we use depend on the deep learning framework for detecting and classi?cation of breast cancer in breast X-Ray images using the concept of transfer learning. The proposed model can detect a mass region and classify it as malignant or benign on X-Ray images in one go with high accuracy. The proposed model is tested on X-Ray images from the open-source Cancer Imaging Archive (CIA) center. The data is preprocessed before giving it to the model. Transfer Learning (TL) is exploited to achieve higher prediction accuracy. Extensive simulations are conducted to measure the performance of the proposed model. The model shows that for ResNet-164 architecture, the best training and validation accuracy is 0.97% and F1 score 0.98%, and the loss rate is 0.02 at an epoch of 40. Conversely, the lowest training and validation accuracy is obtained from AlexNet with 0.91%, and F1 score 0.84%. The loss rate is 0.06 at epoch 40 for the Inception v3 and VGG19 the accuracy is 0.93%, 0.96%, and Fl score 0.89%, and 0.94%, the loss rate is 0.08, and 0.03 at epoch 40 blow-by-blow. Download full text:Check if you have access through your login credentials or your institution Username Password
Abnormal Behavior Analysis Strategy of Bus Drivers Based on Deep Learning

Aiming at the bus driving safety problems caused by the abnormal behavior of the bus driver during the driving process, this paper proposes a deep learning-based analysis strategy for the abnormal behavior of the bus driver. The program defines the abnormal behaviors of bus drivers and categorizes them into behaviors such as smoking, drinking, and making phone calls. The YOLOv5 (You Only Look Once-Version 5) convolutional neural network algorithm is used as the core technique, and the abnormal behavior data of the drivers in the actual bus is used to produce the abnormal behavior data of the bus drivers. Collected and carried out automatic detection experiments to test the feasibility and effectiveness of drivers' abnormal behaviors. The experimental results show that the detection of abnormal behaviors of bus drivers is fast and accurate, the scheme is feasible and effective, and the detection effect can meet the application requirements.
Challenges for higher education in times of COVID-19: How three countries have responded

The COVID-19 pandemic brings to the fore strengths and weaknesses in many public policies, including higher education. There are at least three separate but related areas where institutions of higher learning have been stressed by COVID-19: financing, issues related to the logistics of learning, and inequality. These problems are especially pronounced in countries that suffer from high levels of inequality, such as Chile. This editorial offers a review of some of these challenges and their implication for long-term education policy, touching on the cases of Chile, Canada, and the United States.
Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm

A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.
eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.
Track, Check, Repeat: An EM Approach to Unsupervised Tracking

We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independently of the background. Treating these initial segments as pseudo-labels, we learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We use this ensemble to detect new instances of the "moving" type, even if they are not moving, and add these as new pseudo-labels. Our method is an expectation-maximization algorithm, where in the expectation step we fire all modules and look for agreement among them, and in the maximization step we re-train the modules to improve this agreement. The constraint of ensemble agreement helps combat contamination of the generated pseudo-labels (during the E step), and data augmentation helps the modules generalize to yetunlabelled data (during the M step). We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI, and show strong improvements over the state-of-the-art.
Treewidth-Aware Complexity in ASP: Not all Positive Cycles are Equally Hard

It is well-known that deciding consistency for normal answer set programs (ASP) is NP-complete, thus, as hard as the satisfaction problem for propositional logic (SAT). The exponential time hypothesis (ETH) implies that the best algorithms to solve these problems take exponential time in the worst case. However, accounting for the treewidth, the consistency problem for ASP is slightly harder than SAT: while SAT can be solved by an algorithm that runs in exponential time in the treewidth k, ASP requires exponential time in k · log(k). This extra cost is due to checking that there are no self-supported true atoms due to positive cycles in the program. In this paper, we refine this recent result and show that consistency for ASP can be decided in exponential time in k · log(ι) where ι is a novel measure, bounded by both treewidth k and the size of the largest strongly-connected component of the positive dependency graph of the program. We provide a treewidth-aware reduction from ASP to SAT that adheres to the above limit.
Synthetic-to-Real Domain Adaptation and Refinement

Domain adaptation is a set of techniques aimed to make a model trained on one domain of data to work well on a different target domain. In this chapter, we give a survey of domain adaptation approaches that have been used for synthetic-to-real adaptation, that is, methods for making models trained on synthetic data work well on real data, which is almost always the end goal. We distinguish two main approaches. In synthetic-to-real refinement input synthetic data is modified, usually to be made more realistic, and we can actually see the modified data. In model-based domain adaptation, it is the training process or the model structure that changes to ensure domain adaptation, while the data remains as synthetic as it has been. We will discuss neural architectures for both approaches, including many models based on generative adversarial networks.
Mixed Reality Supported Learning for Industrial on-the-job Training

The digital transformation of industry – the so-called fourth industrial revolution – leads to changing work systems in industry and to new demands on industry workers. New job profiles emerge, existing profiles are changing, and new skills are needed. Thus, continuous and personalized learning on-the-job – directly integrated into the relevant working context – is imperative for 21st century industry workers. New technologies such as Virtual Reality (VR) and Augmented Reality (AR) could be an option for supporting hands-on learning for practical industrial training, directly on the real or virtual machine or factory. These Mixed Reality (MR) technologies are expected to meet the demands for situated and contextual learning according to respective constructivist learning theories. This paper presents two use cases for MR supported learning for industrial on-the-job training in a German manufacturing company focusing on two main research questions: (1) To what extent are MR technologies suitable for implementing key didactic aspects of situated and contextual learning (situational context, active participation, self-control, collective and experiential learning)? (2) What are key characteristics (e.g. type of task, user characteristics, interaction, information and technology-related aspects) for selecting the appropriate technical solution (AR vs. VR) for industrial training? The comparative use case analysis provides some valuable indications for overcoming the limitations of pure physical learning factories and for leveraging their potential for industrial on-the-job training through the targeted use of MR technologies.
Semi-supervised Learning with Multi-Head Co-Training

Co-training, extended from self-training, is one of the frameworks for semi-supervised learning. Without natural split of features, single-view co-training works at the cost of training extra classifiers, where the algorithm should be delicately designed to prevent individual classifiers from collapsing into each other. To remove these obstacles which deter the adoption of single-view co-training, we present a simple and efficient algorithm Multi-Head Co-Training. By integrating base learners into a multi-head structure, the model is in a minimal amount of extra parameters. Every classification head in the unified model interacts with its peers through a “Weak and Strong Augmentation” strategy, in which the diversity is naturally brought by the strong data augmentation. Therefore, the proposed method facilitates single-view co-training by 1). promoting diversity implicitly and 2). only requiring a small extra computational overhead. The effectiveness of Multi-Head Co-Training is demonstrated in an empirical study on standard semi-supervised learning benchmarks.
Fundamental Models in Machine Learning and Deep Learning

The resurgence of applied informatics (AI) has revolutionised the whole of the computing industry, which in turn has revolutionised almost all the possible sectors of industry. AI is the ability of the machines to think and learn in order to solve a problem by making smart decisions. Some of the fundamental and basic machine learning (ML) algorithms include linear regression, logistic regression, support vector machine, random forest, artificial neural networks, decision trees, k-means clustering and Apriori algorithm. Based on these two parameters, ML models can be classified as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Once the labels have been identified for the unlabelled data, any supervised learning models can be used for regression or classification. The hypothesis function for logistic regression is given as where Like linear regression, even logistic regression is non-tolerant with respect to the correlation among input features.
Teacher education in Nigeria and the emerging technologies in the 21st century classroom

The paper examines Teacher Education in Nigeria and the emerging technologies in the 21st century classroom. The concepts; emerging technologies, teacher education, and the roles of teachers in the 21st century classroom were analyzed. Availability, Accessibility and Competency were discussed in the light of the emerging technologies as an instructional mode in the classroom. This is an opinion based paper. It was concluded that the 21st century classroom requires a prepared teacher with an emerging technology-compliance and the right frame of mind. It was recommended among others; government’s policies and legislative approaches that will support in practical terms availability, accessibility and professional development be formulated and implemented to ensure competency and motivation as well as monitoring and evaluation framework.
Privacy Recommendation Based on Bhattacharyya Coefficient

In the era of big data, how to achieve the trade-off between recommendation accuracy and privacy protection has become a hot topic in the field of recommendation. In this work, a recommendation scheme with personal privacy preservation is designed. Besides, it does not depend on co-ratings. In order to solve the problem of excessive sensitivity when the Laplace noise is introduced directly, the Bhattacharyya coefficient that is used as the similarity metric is normalized. Moreover, a personalized privacy protection scheme that considering the difference of users is used for the recommendation system. Experimental results show that, compared with the traditional differential privacy collaborative filtering scheme, the RMSE and MAE of the proposed scheme are significantly improved. The proposed scheme not only ensures the accuracy of the prediction results but also effectively guarantees the privacy protection of user data. It also provides an insight into the design of recommendation services considering privacy protection.
Online Training in Higher Education: an Alternative during COVID-19. Strengths and Weaknesses of Online Training

The article describes online learning against the background of the COVID-19 pandemic in the Bulgarian context of higher education and the challenges faced by university professors during this period and examines their readiness for online learning. The strengths and weaknesses of online learning are considered, as well as the opportunities of higher education to respond to educational problems arising from the COVID-19 pandemic.