Reviewerly/exHarmony huggingface dataset:
Requirement Engineering Challenges in Global Software Development: Empirical Study

Requirements collection is difficult and most critical phase in software development life cycle, in particular in a Global Software Development (GSD) environment. In GSD, clients and vendors are physically separated such that there exists challenges such as lack of face-to-face communication, language differences, culture variation, and time zone differences. This research paper empirically investigates and analyze the identified challenges from Systematic Literature Review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. The results from respondents were analyzed using type of respondents, level of experience of respondents and from client-vendor perspective. The relationships between the challenges and the survey results were evaluated using the Spearman’s correlation coefficient. The results produced a 0.835 Spearman’s correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference.
Interactive Evolutionary Computation with Artificial Bee Colony Method for Multimodal Preferences Retrieval

In this study, we apply an artificial bee colony (ABC) algorithm to the interactive evolutionary computation (IEC) method for the multimodal retrieval of candidate solutions in a Kansei space having multimodal preferences. Previous IEC methods often used a genetic algorithm (GA) method for evolutionary algorithm; retrieving multimodal candidates using these methods were difficult. Therefore, we propose an IEC method with the ABC algorithm for the multimodal and simultaneous retrieval of candidate solutions. We perform numerical simulations with a pseudo user that imitates multimodal preferences as target candidate solutions, instead of a real user. The practical aspect of the proposed method assumes to employ user gaze information for evaluating candidates. Moreover, we set the numerical simulation condition based on a real situation. The results show that the proposed method was more effective for retrieving multimodal candidates than the compared method for practical numbers of candidates and generations.
Layered Cad/Csg Geometry for Spatially Complex Radiation Transport Scenarios

A variety of spatially complex fission, fusion, and national security Monte Carlo (MC) radiation transport scenarios involve combining computer-aided design (CAD) models with traditional constructive solid geometry (CSG) models. A layered geometry method has been implemented in the Shift MC code to address this need. With layered geometry, multiple CAD and/or CSG models can be arbitrarily clipped, translated, rotated, and placed in overlapping layers to form transport-ready geometries. The utility of this method is demonstrated with two problems: (1) a fixed source simulation with a layered geometry consisting of a LiDAR-generated CAD model of the Combined Arms Collective Training Facility urban environment overlaid with CSG models of a mock hotel and a detector apparatus, and (2) a k-eigenvalue calculation using a layered geometry model of the Transformational Challenge Reactor consisting of complex CAD fuel elements placed in a CSG core model. In both cases, tallied particle flux distributions match expectations.
Can Cryptocurrencies Provide a Viable Hedging Mechanism for Benchmark Index Investors?

This paper investigates the non-linear causal nexus at lower, medium, and upper quantiles that cryptocurrencies can generate as a hedging mechanism for international benchmark indices, as represented by the MSCI World index and its sectoral sub-indices. Significant causality-in-mean between each of the assets examined is detected at lower quantiles only in a small number of sectors, whereas all analysed assets are found to trigger important non-linear causal impacts on the volatility of the selected benchmark indices, particularly during periods denoted to possess significant asset price fluctuations. From a financial perspective, this result suggests that cryptocurrencies are developing to possess a very similar ability to long-standing traditional financial assets, to serve as a hedging mechanism against extreme fluctuations within investor portfolios that include MSCI constituents during a variety of market conditions.
The Design of Workplaces with Augmented Reality in Engineering Education

The article deals with the possibility of using augmented reality on the principle of marker target for student education. The whole proposed concept consists of the principle of displaying 3D models of machines, equipment, robots, conveyors, and accessories above the markers depicting an actual device. The first part of the article was focused on the 3D model software tools and was presented a step-by-step procedure for creating an AR application. We will describe a specific strategy for building a 3D model, creating a marker in the Vuforia engine interface. Subsequently, the procedure for working in the Unity development interface up to the final export of the final application was presented. The second part of the article was focused on the developed application and the possibilities of its deployment in the educational process. The initial tests were used by university students on the subject “Digitalisation of Production and Services”. The gained results show the innovativeness of the proposed solution. In this article, an AR application was implemented in the educational process as a new distance education tool during the COVID-19 pandemic. This new way of presenting 3D models using AR falls under the concept of Industry 4.0, which is becoming a necessity today and brings many benefits in education and practice.
A Framework for Active Contour Initialization with Application to Liver Segmentation in MRI

Object segmentation is a prominent low-level task in image processing and computer vision. A technique of special relevance within segmentation algorithms is active contour modeling. An active contour is a closed contour on an image which can be evolved to progressively fit the silhouette of certain area or object. Active contours shall be initialized as a closed contour at some position of the image, further evolving to precisely fit to the silhouette of the object of interest. While the evolution of the contour has been deeply studied in literature [5, 11], the study of strategies to define the initial location of the contour is rather absent from it. Typically, such contour is created as a small closed curve around an inner position in the object. However, literature contains no general-purpose algorithms to determine those inner positions, or to quantify their fitness. In fact, such points are frequently set manually by human experts, hence turning the segmentation process into a semi-supervised one. In this work, we present a method to find inner points in relevant object using spatial-tonal fuzzy clustering. Our proposal intends to detect dominant clusters of bright pixels, which are further used to identify candidate points or regions around which active contours can be initialized.
Maps of Restrictions for Behaviourally Correct Learning

In language learning in the limit, we study computable devices (learners) learning formal languages. We consider learning tasks paired with restrictions regarding, for example, the hypotheses made by the learners. We compare such restrictions with each other in order to study their impact and depict the results in overviews, the so-called maps. In the case of explanatory learning, the literature already provides various maps. On the other hand, in the case of behaviourally correct learning, only partial results are known. In this work, we complete these results and provide full behaviourally correct maps for different types of data presentation. In particular, in all studied settings, we observe that monotone learning implies non-U-shaped learning and that cautiousness, semantic conservativeness and weak monotonicity are equally powerful.
Bootstrapping Via Meta-Learning Approach

Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging metaoptimisation problem. We propose an algorithm that tackles this problem by letting the meta-learner teach itself. The algorithm first bootstraps a target from the metalearner, then optimises the meta-learner by minimising the distance to that target under a chosen (pseudo-)metric. Focusing on meta-learning with gradients, we establish conditions that guarantee performance improvements and show that the metric can control meta-optimisation. Meanwhile, the bootstrapping mechanism can extend the effective meta-learning horizon without requiring backpropagation through all updates. We achieve a new state-of-the art for model-free agents on the Atari ALE benchmark and demonstrate that it yields both performance and efficiency gains in multi-task meta-learning. Finally, we explore how bootstrapping opens up new possibilities and find that it can meta-learn efficient exploration in an É›-greedy Q-learning agent - without back propagating through the update rule.
Development and Validation of a Deep Learning Model to Predict Axial Length from Ultra-Wide Field Images

Background: Axial length (AL) is one of the most important parameters in ophthalmological practice. However, measurements of AL require specialized equipment, expertise and time. Methods: This study included 6174 ultra-wide field images from 3134 patients who visited the Eye and ENT Hospital of Fudan University from December 2014 to June 2020. Inception-ResNet-V2 was employed as the model structure. Of 6174 images, 4939 were used for training, 617 for validation, and 618 for testing. The coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were used for model performance evaluation. Findings: Our model predicts axial length with high accuracy (R2, MAE and MSE were 0.579, 1.419 and 0.9043, respectively). Prediction bias of 64.88% of the tests was under 1-mm error, 76.90% of tests was within the range of 5% error and 97.57% within 10% error. The prediction bias had a strong negative correlation with measured AL values and a weak correlation with sex. Generated heatmaps demonstrated that the model focused on typical pathological changes such as tessellated fundus and peripapillary atrophy in high myopia fundus. In sex-specific models, R2, MSE, and MAE of the female AL model were 0.411, 1.357, and 0.911 in female data and 0.343, 2.428, and 1.264 in male data. Three metrics of male AL models were 0.216, 2.900, and 1.352 in male data and 0.083, 2.112, and 1.154 in female data. Interpretation: Accurate AL prediction can be achieved with a deep convolutional neural network and UWF images.Funding Information: 1) National Natural Science Foundation of China (Grant No. 81770955) 2) Joint research project of new frontier technology in municipal hospitals (SHDC12018103) 3) Project of Shanghai Science and Technology (Grant No.20410710100) 4) Clinical Research Plan of SHDC (SHDC2020CR1043B) 5) Project of Shanghai Xuhui District Science and Technology (2020-015) 6) Shanghai Rising-Star Program (21QA1401500)Declaration of Interests: Authors declared no conflict of interests.Ethics Approval Statement: The single-center cross-sectional study was conducted with approval from the Ethics Committee of Eye and ENT Hospital of Fudan University. All procedures abided by the principles of the Declaration of Helsinki.
A quantitative description of the voltammetric time series of unclosed electric circuits for detection of differences between different potentiostats/galvanostats

In this paper, ideal boundary conditions have been created for recording instrument noises and their fluctuations at the level of pico-currents to identify the best “ideal” device using voltammetric time series.
Challenges of Applying Distance Technology to the Study of History (based on the results of a sociological survey of students in Togliatti)

The article discusses the problems of distance learning. They are relevant due to the increase in the demand for continuing education, the development of teaching methods for humanities, particularly history. Distance learning in history is related to the task of shaping historical thinking. The purpose of the article is to study the attitude of university lecturers and students towards distance learning in history classes. The empirical basis of the article was the results of a sociological survey conducted among the students of Togliatti State University in 2021 (N=287).The analysis of the survey results shows that the students’ views on distance learning in history are divided. About half of the students are positive about this mode of study, and the other half are negative. There were more respondents among young males, as well as technical students, who described the history distance learning sessions positively. The students are attracted by the extensive use of Internet sources and visual materials. Among girls, as well as humanities students, there are more of those who have a negative attitude towards distance learning in history. The negative aspects are related to technical problems, as well as the lack of direct communication with the teacher. The respondents who do not have a high level of interest in studying history are more likely to express a positive attitude towards distance learning. In contrast, the respondents who are interested in history are more likely to have a negative attitude towards this form of training.
Artificial Intelligence Empowered Internet of Things for Smart City Management

The Research on the Internet of Things (IoT) has paved the way for a revolution in community services. It was found that the application of IoT in a smart city is mainly carried out without major human intervention. The different uses of IoT devices allow interfaces to available systems, improve communication and perform a variety of tasks. In addition, the availability of multiple IoT devices has been shown to be compatible to protect integrity, collect and analyze information, and improve functionality. The study is focused in understanding the critical role Artificial Intelligence empowered Internet of Things (IoT) for creating Smart city. The researchers have used extensive data analysis by collecting the data from the respondents to understand the importance of AI empowered IoT in creating better smart city. The analysis is focused in using regression tools and other key data analysis to test the hypothesis. Based on the overall analysis it is concluded that enhanced security and privacy; implementing smart sensors; implementation of Intelligent analytics and better collaboration and Networking has supported the organization and other stakeholders in creating better smart city.
Study of Adequacy of Drainage Channels in Suburban Residential Areas(A Case Study of Residential Wirolegi Regency 2 Jember City, of Indonesia)

Wirolegi Regency 2 Housing is located on the outskirts of the city which is located in Pakusari District, Jember Regency, when there is quite heavy rain, puddles of water often occur, this is caused by environmental damage. The impact of environmental damage can be of various kinds, one of which is due to climate change. In the dry season, it can have an impact on drought and in the rainy season, it can have an impact on flooding. When there is high rainfall, it has the potential to cause flooding in the residential area. Another factor is the condition of the existing drainage channels which are inadequate and affect the occurrence of flooding in the housing area. On the basis of the above, it is necessary to conduct a research entitled "Study on Adequacy of Drainage Channels in Suburban Residential Areas". From the results of the study, it was concluded that the watershed in the residential area of ​​Wirolegi Regency II Jember has a watershed area of ​​148.75 km2. The value of rainfall intensity at the 5-year return period is 9.02 mm/hour, the design flood discharge value is 12131.7 m3/second, the runoff discharge value is 1473.8 m3/second, the total discharge is 157.280 m3/sec, dirty water discharge is 7.058 m3/second, total capacity discharge is 84.7 m3/second. For the drainage network, there are primary, secondary, and tertiary canals, totaling 3 outlets where all outlets lead to rivers. For channels that are not able to accommodate discharge, namely channels F, G, H, I, J, K, L, and M channels. For channels that require repairs, the dimensions of the channel are changed from 0.8 meters to 1 meter. It is recommended for each housing estate to pay attention to the discharge flowing in each channel by using a calculation formula that is in accordance with the conditions and criteria requirements of the formula and method used.
Handwritten Digit Recognition with Neural Network

Object detection is the task of classifying and finding certain objects including people, cars, handwritten text, and more. This is a growing field with application in several fields, identifying and locating cars, pedestrians and other objects for self-driving cars, monitoring objects such as crops, or the ball during sports, facial detection for protection and many more. Handwritten text recognition comprises character recognition and digit recognition. An effective CNN based model with high accuracy can help an OCR (Optical Character Recognition) system for accurate conversion of written text into digital text. In this work, we have explained a CNN based approach for recognizing hand written digits and predict the written numbers.
Hudnet: A Dynamic Calibration Method for Automotive Augmented Reality Head-Up-Displays

The automotive augmented reality head-up-display (AR-HUD) system relies on multiple free-form surfaces to project the virtual image into human eyes. A wide range of views and the extensive display area result in extraordinarily complex distortions. Methods widely used for calibration, such as look-up tables and interpolation, require considerable memory capacity, complicated calibration procedures, etc. In this context, we propose a fully connected neural network (HUDNet) for an automotive AR-HUD to correct dynamic distortion. Here, we propose a parallel prediction framework for the center and edge of the image, and separate the distortion information from the disparity information in order to balance the unstable central field of view error. Ultimately, we introduce the transfer learning method to improve accuracy. The present study provides a reference for us to apply deep learning methods to predict dynamic image distortion of various complex multi-free-form surface reflection systems in the future.
Humanitarian Technologies in the Education System of Modern Russia

Purpose: The article considers the problems of introducing new technologies into the Russian education system, due to the objective needs of increasing the pace of information and technological support in all fields of public life. Design/methodology/approach: In the case of public approval of the objectively inevitable introduction of information technologies into education, there are some fears caused by the fact that among the goals of informatization and digitalization of the education system, educational goals may dominate, because of which socio-educational and worldview goals will not be taken into account. Findings: The possible positive and negative consequences of the use of modern humanitarian and information technologies in education are being described: the reduction and exclusion from the educational process of classical methods of education, built on direct interpersonal communication between students and teachers, which ensure the formation of personal qualities, a positive worldview, citizenship and patriotism among students. Originality/value: It has been proved that the development of computer programs for educational organizations usually takes into account the priority solution of educational tasks; as a result of which, the problems of using humanitarian and information technologies to educate students are not always solved. As a result, educational work in educational organizations should be carried out by pedagogical workers who are able to control the content of information and use it to educate students using humanitarian technologies. The author's definition of modern humanitarian technologies in education is formulated; their capabilities and role in the conditions of such trends of social development are outlined, as globalization, informatization, digitalization, revealed the results of the symbiosis of humanitarian and information-communicative, and nano-bio-cognitive technologies transforming traditional patterns of human-world interaction, changing the logic of social behavior of people on a global scale; the conclusion that there is an increase in the need for training teachers is justified; those with knowledge of humanitarian education technologies are offered ways to train such teachers in the system of additional pedagogical education.
Hybrid Intelligence Approaches for Distributed Information Management

In this paper, new approaches for secure information management will be presented. Such approaches use the hybrid human-AI methods oriented for security technologies. Such paradigm support management processes proposed for particular persons, thanks to the involvement of AI approaches. Such methods are designed for efficient data protection in distributed systems. Application of such procedures allow to significantly increase the efficiency and security of management strategies.
Polarity Classification of Sarcastic Sentence Patterns Based on N-Gram Technique for Twitter Dataset

At present, Micro-blogging has enhanced an integral part of modern life, allowing individuals and organizations to communicate their thoughts and opinions in a variety of ways. Due to the rise of social media platforms, people from various countries have started using blogs and Twitter to share their thoughts and feelings which are expressed as tweets. The task of recognizing the perspective or an attitude of the user that is represented on a topic and is called sentiment analysis. Sentiment analysis is used to designate text or words into a diversity of sentiments. The paper proposed the method for classifying the polarities of sarcastic sentences. Initially, data is pre-processed and feature extraction is done that includes tokenization, lemmatization. To withdraw the word frequency is the most primary form of analyzing textual data. The one only sentence is tiny for an entity for noticing the word distribution. Therefore, the evaluation of the word frequency is done on all positive sentences. Further, we use machine learning for identifying polarities of sarcasm in the sentence. Polarities are deliberated for the patterns based on their structure and the features of phrases in the sentences. The experimental results are favorable with good accuracy for polarities of sarcastic sentences based on the performance metrics.