Reviewerly/exHarmony huggingface dataset:
User Stories identification in software's issues records using natural language processing

Nowadays most of software development companies have adopted agile development methodologies, which suggest capturing requirements through user stories. The use of these good practices improves the organization of work teams and the quality of the resulting software product. However, user stories are too often poorly written in practice and exhibit inherent quality defects. In addition, it is common to find the user stories of a software project immersed in large volumes of issues request logs from software quality tracking systems, which makes difficult to process them later. In order to solve these defects and to formulate high quality requirements, a current trend is the application of computational linguistic techniques to identify and then process user stories. In this work, we present two recurrent neural network models that were developed for the identification of user stories in issue records from software quality tracking systems for further processing.
Quantifying the Demand for Explainability

Software that uses Artificial Intelligence technology like Machine Learning is becoming ubiquitous with even more applications ahead. Yet, the very nature of these systems has made it very hard to understand how they operate, creating a demand for explanations. While many approaches have been and are being developed, it remains unclear how strong this demand is for different domains, application types, and user groups. To assess this, we introduce a novel survey scale to quantify the demand for explainability. We also apply this scale to an exemplary set of applications, novel and traditional, in surveys with 212 participants, showing that interest in explainability is high in general for intelligent systems but also traditional software. While this validates the heightened interest in explainability, it also reveals further questions, e.g. where we can find synergies or how intelligent systems require different explanations compare to traditional but equally complex software.
Note Onset Detection using Echo State Networks

In music analysis, one of the most fundamental tasks is note onset detection– detecting the beginning of new note events. It is the basis for more high-leveltasks, such as beat tracking or tempo detection. The main outline of all approachesfor onset detection is roughly the same: The audio signal is transformed into anOnset Detection Function (ODF), which is zero for most of the time but has pronouncedpeaks in case of onsets. Applying peak picking algorithms on the ODF, theonset times can be extracted. Currently, Convolutional Neural Networks (CNNs)define the state of the art. In this paper, a first exploration of Echo State Networks(ESNs) to obtain an ODF is presented. ESNs have achieved comparable results toCNNs in several recognition tasks, such as speech and image recognition. Featureswere extracted using a bank of filters with a logarithmic frequency spacing. Thefeature vectors were fed into the ESN that computed the ODF. Applying a simplethreshold-based peak picking algorithm on the ODF, the onsets were detected.For the hyperparameter optimization, a dataset with pre-defined splits for an 8-foldcross validation was used. With all hyperparameters optimized, we reached anF-Measure of 0.812 using a bidirectional ESN with 8000 neurons.
Assessment of Digital Literacy Competence for Non-Formal Education Study Program Students

Purpose: Current educational transformation focuses on the process of utilizing information and communication technology. What's more for students, digital technology makes it easy to access, share, analyze, and present information obtained from various digital sources. The research aims to develop indicators and assess students' digital literacy competencies. Methods: This research is a descriptive quantitative study. The population taken was students of non-formal education at the State University of Padang. The sample was taken 117 respondents using a purposive sampling technique. The data was collected by distributing questionnaires. Measurement of the level of digital literacy based on the Likert scale (1-5), the results are interpreted as never, rarely, sometimes, often, and always. Results: Of the 117 students, 45.01% of students often use and choose digital media in learning and digital media can complete assignments. 37.61% of students had never checked a trustworthy website and on a different website. 30.39% of students never post the latest information they get. 38.46% of students never communicate via the internet with colleagues from other disciplines and professional experts to complete assignments. 43.02% of students never used the internet to search for ideas through the internet to improve products and find new techniques in completing assignments. Conclusion: This study reflects the initial ability and willingness of non-formal education students to develop digital literacy competencies which in turn provide opportunities for education and policymakers to develop curricula and learning based on information and communication technology.
Understanding migrants in COVID-19 counting: Rethinking the data-(in)visibility nexus

Abstract The COVID-19 pandemic confronts society with a dilemma between (in)visibility, security, and care. While invisibility might be sought by unregistered and undocumented people, being counted and thus visible during a pandemic is a precondition of existence and care. This article asks whether and how unregistered populations like undocumented migrants should be included in statistics and other “counting” exercises devised to track virus diffusion and its impact. In particular, the paper explores how such inclusion can be just, given that for unregistered people visibility is often associated with surveillance. It also reflects on how policymaking can act upon the relationship between data, visibility, and populations in pragmatic terms. Conversing with science and technology studies and critical data studies, the paper frames the dilemma between (in)visibility and care as an issue of sociotechnical nature and identifies four criteria linked to the sociotechnical characteristics of the data infrastructure enabling visibility. It surveys “counting” initiatives targeting unregistered and undocumented populations undertaken by European countries in the aftermath of the pandemic, and illustrates the medical, economic, and social consequences of invisibility. On the basis of our analysis, we outline four scenarios that articulate the visibility/invisibility binary in novel, nuanced terms, and identify in the “de facto inclusion” scenario the best option for both migrants and the surrounding communities. Finally, we offer policy recommendations to avoid surveillance and overreach and promote instead a more just “de facto” civil inclusion of undocumented populations.
Analysis of the Novel Transformer Module Combination for Scene Text Recognition

Various methods for scene text recognition (STR) are proposed every year. These methods dramatically increase the performance of the existing STR field; however, they have not been able to keep up with the progress of general-purpose research in image recognition, detection, speech recognition, and text analysis. In this paper, we evaluate the performance of several deep learning schemes for the encoder part of the Transformer in STR. First, we change the baseline feed forward network (FFN) module of encoder to squeeze-and-excitation (SE)-FFN or cross stage partial (CSP)-FFN. Second, the overall architecture of encoder is replaced with local dense synthesizer attention (LDSA) or Conformer structure. Conformer encoder achieves the best test accuracy in various experiments, and SE or CSP-FFN also showed competitive performance when the number of parameters is considered. Visualizing the attention maps from different encoder combinations allows for qualitative performance.
Binary classification of single qubits using quantum machine learning method

Abstract Machine learning can make predictions on unseen data by extracting information and constructing algorithms and statistical models from the training data, which is highly desirable for speech recognition and computer vision. Quantum computing harnesses the phenomena of quantum mechanics realizing parallel computing, which could solve certain computational problems, such as RSA encryption significantly faster than classical computers. Quantum machine learning is reasonable to enable machine learning faster than that of classical computers by utilizing quantum operations. Binary classification is a typical quantum machine learning task applied to single qubits. Here we present a binary classifier for regions. Firstly, we sample two sets of quantum data points located in the X-Z plane of the Bloch sphere randomly. Then we utilize a single parameterized rotation gate and measure the qubits along the Z axis. We train the hybrid model combined by quantum mdoel and classical Neutral network and regard the corss entropy the predictions of the classical Netural network and labels as the loss function. Based on the above, we present the results of the trained hybrid model to classify new quantum qubits.
Vehicle behavior analysis using reconstructed 3D parameters for road safety

Road traffic safety is a very important issue in the field of intelligent transportation system (ITS). Vehicle segmentation and behavior analysis are an significant part for solving this problem. However, 2D image detection technology is difficult to reconstruct missing information of damaged image. In this paper, a bottom-up analysis method is employed to study the related technical problems, and it will provide a strong data foundation for road traffic safety. Firstly, the M-BRISK descriptor algorithm is proposed to describe the local feature points. Secondly, we propose a 3D feature analysis method based on rigid motion constraints for vehicle trajectory. Thirdly, a similarity measure method is proposed for trajectory clustering. Finally, we used the obtained 3D information of vehicles to analyze the vehicle behavior to find the abnormal vehicles for road traffic safety. The experimental results confirm that the M-BRISK descriptor performs well comparing with the state-of-the-art feature descriptors, and the proposed clustering method improves the accuracy of the trajectory clustering. Moreover, the vehicle motion information contained in the trajectory data can be analyzed to recognize vehicle behavior. The presented work in this paper provides an important foundation for vehicle abnormal behavior detection for road traffic safety.
Real-Time Vehicle Object Detection Method Based on Multi-Scale Feature Fusion

Existing object detection algorithms are affected by scenes with poor robustness, besides the existing public datasets are not applicable to urban road traffic scenes. In order to solve the problems of low accuracy in detecting panoramic video images, high false detection rate, this article designed a real-time traffic information detection method based on multi-scale feature fusion. For a start, the vehicle equipped with hp-f515 driving recorder collected video under the real road scene in Beijing. The total length of the driving route was 11 km. Extracted the recorded video, divided the video into frames which size was 1920 pixel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times1080$ </tex-math></inline-formula> pixel, the classification was based on the type of vehicles in the general road landscape, and the format of datasets was Pascal VOC. Subsequently, an improved SSD (Single Shot Multi Box Detector) detector was designed, used single-data deformation data amplification methods to perform color gamut transformation and affine change on the original data to generate new data types; utilized the learning rate-adaptive adjustment algorithm to improve the efficiency of detector training. Eventually, the detector was used to detect traffic information in actual road scenes. The experimental results were compared with other traditional detectors. Extensive detection experiments showed that the detector had a processing speed of 55.6ms/frame with an accuracy rate of 98.53%, which could accurately identify multiple objections, small-distant objections, and overlapping objections in actual road scenes. It could provide the advanced driving assistance system with the perception information of the surrounding environment in time.
Estonia’s e-governance and digital public service delivery solutions

With the rising digitization and efficiency in digital service delivery, Estonia is a good example of how technology has revolutionized the traditional approach of governance and delivery of services by the government. This paper has presented a review of the current ecosystem of various e-governance initiatives in Estonia by reviewing major service delivery modules and initiatives. This paper also intends to deliver a policy framework and technological model for developing economies while focusing on government bodies and non-profits to embrace digitization mechanisms.
Why Compliance is needed for Internet of Things?

The Internet of Things (IoT) is a relatively new concept that has been coined and is now commonly used in the field of Information Technology (IT). This concept is defined as many things, but the simplest way to describe IoT is as the culmination of all of the new devices, systems, applications, technology, etc. that are connected and can transmit data without the need of a human or computer. This developing industry is expected to have billions of devices interconnected in the next few years. This growth is happening in all branches of traditional IT. Different divisions of IT, such as the financial division or medical division of IT, are already moving to the IoT. While this technological advancement is conceptually astounding, just like anything new in the world of IT, there arise many questions about the security of, or lack thereof, IoT. The main purpose of this paper is not to come up with a solution to the lack of security compliance of IoT but identify that the lack of compliance laws for IoT is a problem and that traditional IT compliance laws do not work for the IoT unless modified. Instead, new compliance standards should be established to cover the subject of IoT.
A Study of Cobot Practitioners Needs for Augmented Reality Interfaces in the Context of Current Technologies

Human-Robot Interaction (HRI) for collaborative robots has not changed since the introduction of the first cobot. The main interface to communicate with the robot remains a wired display - teach pendant (TP). While attempts are made to make the programming experience better - more intuitive touch-screen displays, it generally remains the same. With the recent rapid development of Augmented Reality (AR), the HRI of the cobot could drastically change. This paper explores AR-based implementations in robotics and categorizes them based on the type of the used device, with the main focus on the least explored category - mobile AR. Furthermore, two experiments are conducted to determine the user’s experience in robot programming using TP with a mobile-based AR interface. For this reason, an AR application prototype is developed as a co-interface to a TP. The results of the experiments are presented: the first examines the user’s needs that are missing in current solutions, while the second one analyses the user’s experience in using the robot with the AR interface. The obtained results suggest that users could benefit from mobile-based AR solutions in the commissioning and troubleshooting phase of the lifetime of the robot. However, at the same time, this solution is not advanced and accurate enough (yet) to encourage users to switch to the new platform and abandon the classical TP, while programming the robot.
Algebraic Bayesian Networks: the Generation of the Knowledge Pattern Canonical Representation

Algebraic Bayesian networks relate to the class of probabilistic graphical models. The data in the network is divided into smaller parts - knowledge patterns. Each knowledge pattern can be presented as a quanta set. Each element of the knowledge pattern has a scalar or interval estimates of probability. The handling of networks with scalar estimates has large complexity. This is why one of the tasks during working with Algebraic Bayesian networks with interval estimates is to find the canonical representation of these networks. The canonical representation is the network with scalar estimates, which presents the reduced information from the first one. The task of this study is to generate the canonical representation for a separately taken knowledge pattern. This task is solved with the usage of Monte-Carlo method, approach and algorithm are described, an example of algorithm's usage is shown and a naive approach for generation of the whole network canonical representation is proposed.
A Fast and Efficient Super-Resolution Network Using Hierarchical Dense Residual Learning

In deep convolutional neural networks (DCNNs) for single image super-resolution (SISR), the dense and residual feature refinement helps to stabilize the training network and enriches the feature values. However, most SISR networks do not fully exploit the rich feature information in the hierarchical dense residual connections, thus achieving relatively low performance. Besides, in many cases, a large model is not feasible to deploy on mobile or embedded devices. By exploiting the hierarchical dense residual learning, this paper proposes a fast and efficient hierarchical dense residual network (HDRN) to solve these problems. Specifically, we develop a dense compact residual group (DCRG), consisting of several compact residual blocks (CRB), which helps to increase the reusable feature capability. Our experimental results confirm that the proposed HDRN achieves better trade-off between the performance and computational costs than those state-of-the-art lightweight SISR methods.
Garbage Collect Your Technical Debt

There is a kind of design distortion that happens when a team chooses to build iteratively instead of looking at all of the requirements at once. Ward Cunningham coined the term technical debt to describe those design distortions.1 By understanding the causes of tech debt and connecting them back to a team’s actions (or inactions), it’s possible to minimize the buildup of tech debt and keep a system healthy indefinitely. The way to minimize tech debt is to view a software development process as an algorithm, consider several algorithms, and choose the right one for the circumstances.
A Descriptive Answer Evaluation System Using Cosine Similarity Technique

Use of computer in spite of human in knowledge discovery and engineering is a trend from last decade. In situation of COVID-19, it is need of hour to use such computer assisted assessment tools which will reduce the work of teachers in education domain. In Literature various techniques were developed to assess the answers of objective types questions by using machine learning but very less work carried out on assessment of answer of descriptive type questions. This paper provides a solution for the assessment of answer of descriptive type questions where teacher need not to use any paper or pen but the computer imitates like as teacher and evaluate answer of students. The primary objective is to represent results of subjective answers in the form of pictorial representations using cosine similarity technique. Cosine similarity is one of similarity measures which find similarity between two objects irrespective of their size. The results of web-based application are sufficient enough to withdraw the traditional teacher centric assessment approach and develop a computer-aid solution which is beneficial for educational institutions.
The Model of STEM Education as an Innovative Technology in the System of Higher Professional Education of the Russian Federation

To study STEM and STEAM education as an innovative technology, the authors of the paper conducted a comprehensive theoretical and methodological study on the stated problem. A comprehensive theoretical and methodological study was directed towards the solution to the problem of developing a new model for the STEM and STEAM education system to make correct management decisions in the framework of the country's socio-economic development based on objective data. The object of the study was STEM and STEAM education as an innovative technology. The subject of this study is the mechanisms of STEM and STEAM-education in the system of higher professional education of the Russian Federation. The methodological foundation of the study was the general scientific ideas for holistic, systemic, and integrative approaches to the study, which gave an opportunity to assess a set of interrelated and interacting elements that form a certain integrity. The use of these approaches provided a comprehensive analysis and formulation of the research problem and determined the strategy for its study. Both theoretical and empirical methods of studying the indicated problem were used in the comprehensive theoretical and methodological study presented in the paper. The leading methods of scientific knowledge were the following: the method of comparison; phenomenological data analysis; discourse analysis of the text; theoretical analysis; empirical description (expert assessment method and in-depth interviews). The result of the study is the STEM education model developed by the authors as an innovative technology in the system of higher professional education of the Russian Federation. The materials of the paper will be useful for specialists in the field of higher professional education, teachers of natural sciences and humanities, the teaching method of which is based on the STEM approach.
Handling of Labeling Uncertainty in Smart Homes using Generalizable Fuzzy Features

Smart homes research is now entering a phase of real deployment and of early commercialization. For the type of smart homes used to monitor the daily life of residents, activity recognition is one of the key artificial intelligence components necessary. In labs, it is mostly based on machine learning methods, but in real deployments, due to the difficulty to build labeled datasets, it still usually depends largely on logical systems and inference rules. In this work, we try to leverage generalizable fuzzy features to evaluate the quality of the label inferred by commonsense inference. The fuzzy rules are built from annotated instances in CASAS's dataset and by transferring them to our own infrastructure. The data exploited include 11 of our deployed smart homes and shows promising results. Our experiments shows that it is likely possible to exploit those rules to evaluate the quality of our data labeling.
Research on Recommendation Algorithm Based on Collaborative Filtering

In view of the many shortcomings of the current industry recommendation system, this article has carried out meaningful and valuable in-depth research and exploration on the collaborative filtering recommendation algorithm [1] [2] adopted by the personalized news recommendation system, hoping to help users accurately from the huge amount of news ocean Quickly find interesting news and get a good user experience. The main tasks of this paper are: 1) Conduct an in-depth study and review of related technologies for personalized news recommendation. 2) Propose an improved collaborative filtering recommendation algorithm based on user item hybrid model. 3) Design and implement a personalized news recommendation system [3] [4] based on a collaborative filtering recommendation algorithm based on an improved user item hybrid model [5]. The experimental data proves that this system has a good personalized recommendation function, and the personalized news recommendation system based on this algorithm is more effective. The recommendation effect of the traditional personalized recommendation system has been significantly improved.