Reviewerly/exHarmony huggingface dataset:
Effective Communication in Globally Distributed Scrum: A Model and Practical Guidance

A trend in information systems development is for globally distributed teams to use agile methods and frameworks such as Scrum. In globally distributed (GD) software development, a known challenge is effective team communication. Researchers, however, cannot evaluate effective communication in GD teams using Scrum unless they know what effective communication means in that context. This qualitative study contributes a theoretical model of effective communication in GD Scrum teams and practical guidance for practitioners. Ten industry professionals working in GD Scrum teams were interviewed to capture their understanding of effective communication. Qualitative content analysis was used to analyse the interviews and form a basis for the model and the practical guidance. This novel model consists of communication transparency, communication quality, and communication discipline, which together lead to the alignment of team understanding (i.e., a team-level shared mental model). This theoretical model lays the ground for future research into the effect of Scrum practices on communication in GD contexts, and the effect of communication on team and project success. For practitioners, this study contributes 11 practical actions that professionals recommend for improving and sustaining effective communication.
Designing Digital University Conceptual Framework; Grounded Theory Approach

The present article seeks to identify the characteristics of the digital university using grounded theory. Snowball sampling was performed and 19 experts in the field of university and digital transformation were interviewed. The result of the interviews was a collection of basic topics that were collected during the open coding process and categories were extracted from them. Then, in the central coding stage, the link between the categories under the following headings: causal conditions, contextual factors, intervening conditions, phenomenon-oriented, strategies and consequences of digital university was determined. Then the cases were written in the selective coding stage. The causal conditions affecting the digital university were divided into two categories. Factors within the university: the need to rethink the mission of the university, the need to change teaching and research approaches, the cost and bureaucracy of education and the need to use new educational opportunities and factors outside the university: demographic challenges, the need to identify and meet scientific needs and job for society. Such are the policy and management strategies of research. In policy strategies, policy of education, hard and soft technologies, digital culture, finance and management and data preservation and educational equality are suggested, and in management strategies (executive), assessing the level of digital maturity of the university, providing infrastructure in humanities, leadership, economics and technology, and review of the education system process and definition of new processes are suggested.
“Real Deed of an Artist” and Philosophy of Art: Solovyev – Fyodorov – Chekrygin

The article is dedicated to the aesthetic views of an artist and philosopher V.N. Chekrygin (1897–1922). His ideas are compared to the philosophy of art of V.S. Solovyev and N.F. Fyodorov. Theoretical heritage of the artist is analyzed, it reflects understanding of the tasks of creativity, history and language of the visual art. The research is based on the texts of Chekrygin: “A Manifest of the Painters (1920)”, “The Program of One-year Course on Art Philosophy” (1920), “About Upcoming New Era of the All-European Art” (1921), “About Convocation of the Resurrecting Museum’ (1921), and his epistolary art. Little-known materials from the archive are considered. Studying aesthetic works of Fyodorov and Solovyev allows to make comparative-typological research of the artist and philosopher of art. The connection is shown between Chekrygin’s ideas and aesthetic tradition of the Russian religious philosophy. Signs of aesthetics of Christian universalism can be seen in his project of a new era of the art. The idea of the project of “living art” of Chekrygin is opposed to the research of avantgarde art and it was inspired by the ideas of conciliarity and theurgy. It is shown that philosophical-aesthetic ideas of the artist are close to the philosophy of art of V.S. Solovyov and even more to the doctrine of the aesthetic supramoralism of Fyodorov. The research discloses particularities of the aesthetic ideas of the artist, which are permeated with the spirit of eschatological anxiety. Chekrygin shows the way that modern art should follow referring to the topics of theophany, to the process of contemplation of the space evolvement and the creation of the church-museum of the universal resurrection. It is proved that the artist creates the image of the final ideal of art conceiving the philosophical ideas of Solovyov and Fyodorov. It is proved that the artist remains in line with modernism discourse at the same time creating the outlines of the art of the future.
Joint User Association and Resource Allocation for Hierarchical Federated Learning Based On Games in Satisfaction Form

Hierarchical Federated Learning (HFL) has emerged to overcome the shortcomings of conventional Federated Learning (FL) due to communication obstacles between the end users and the cloud server and the congestion at the backhaul of wireless network implementations. In this paper, we consider a wireless user-edge-cloud HFL network where the transmissions of the users’ local model parameters to the edge are multiplexed via the Non-Orthogonal Multiple Access (NOMA) technique. The joint problem of association and uplink transmission power allocation of the users to the edge is formulated and solved as a non-cooperative game in satisfaction form. Diverging from the prevailing research that proposes centralized solution concepts, each user makes autonomous decisions regarding its association and power level so as to attain a minimum acceptable tradeoff of three vital network factors. The latter includes the global model’s training accuracy and the users’ consumed energy and time during transmission. Different types of equilibria are explored, i.e., the Satisfaction Equilibrium (SE) and Minimum Efficient Satisfaction Equilibrium (MESE) which not only fulfills users’ minimum tradeoff but also minimizes the overall network’s cost. Algorithms based on Reinforcement Learning (RL) and Best Response Dynamics (BRD) are, then, devised to conclude the SE and MESE points. The proposed framework is evaluated via modeling and simulation, verifying its efficiency in achieving an equitable balance in the network.
Spatial Analysis of Hypsometric Characteristics of the Wadi (Tagharan) Basin

This study aims to analyze the spatial variation of the Hypsometric characteristics of the Wadi (Tegran) basin, and to find the spatial relationship between the values of this characteristic and the values of the morphometric characteristics of the 35 secondary basins. The inductive approach and the spatial statistics method were relied upon to reach the goal of the study, and the tools of spatial statistical analysis in the (ArcMap) program were used, including spatial regression analysis. The study concluded that the shape of the main Hypsometric curve of the basin (Tagaran) indicates that the basin has reached an advanced stage of the geomorphological cycle, where we notice the clear concavity of the curve shape. the value of the Hypsometric integration was (0.28), which is evidence that the basin reached an advanced stage (monadnock)) of the stages of the erosion and sculpting cycle. Most of the secondary basins (21) have concave-convex Hypsometric curves, which are the equilibrium or maturation stage in the geomorphic cycle, The values of the hypsometric integration ranged between (0.36-0.47). It turns out that the values of the Hypsometric integration do not form a specific spatial model, And the spatial relationship between the values of the hypsometric integration and the quantitative characteristics of the secondary basins is negative and not strong.
UAV Coverage Path Planning with Quantum-based Recurrent Deep Deterministic Policy Gradient

This study proposes quantum-based deep deterministic policy gradient (Q-DDPG) and quantum-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recurrent</i> DDPG (Q-RDDPG) schemes for time-series optimization in UAV communications. Herein, Q-DDPG-based actor-critic reinforcement learning is utilized to optimize action selections in a large state and continuous action space. In this scheme, quantum models are exploited to reduce computational complexity and training loss. As a particular case, Q-DDPG and Q-RDDPG are employed for trajectory optimization and dynamic resource allocation in UAV communications. The results demonstrate that Q-DDPG and Q-RDDPG schemes achieved higher rewards with lower training losses compared to classical DDPG.
Parallel Intelligence-enabled IoV Information Management System based on Blockchain

The current Internet of Vehicles (IoV) data are facing challenges such as data silos, security and privacy concerns, data quality issues, and collaboration barriers. This paper proposes an IoV Information Management System (IIMS) based on parallel intelligence and blockchain to address these challenges. By leveraging the decentralized, immutable, and traceable nature of blockchain, combined with the incentive mechanisms and collaborative models of decentralized autonomous organizations (DAOs), a secure and trustworthy data sharing platform is established for collaborative analysis, business cooperation, and innovative applications of IoV data. The proposed approach utilizes the decentralized nature and data backup capabilities of blockchain to mitigate the single point of failure issues associated with centralized servers. Additionally, the anonymity, immutability, and traceability features of blockchain ensure the privacy, security, and integrity of data sharing. Moreover, the incentive mechanisms and DAO solutions within the blockchain promote data sharing among vehicles while ensuring data quality.
Dynamic Fitting Multi-Task Network for Multi-PAR in Smart City

Multi-pedestrian attribute recognition (MPAR) is an important task for smart city surveillance. Most existing works focus on the scenario with simple background, lacking performance and accuracy for practical deployment. There needs a comprehensive solution for multi-pedestrian attributes recognition which can perform multi-pedestrian detection, pedestrian attribute recognition, pedestrian re-identification in a well-designed inference network to meet both accuracy and performance requirements. This paper proposes a novel end-to-end Dynamic Fitting Multi-Task Network (DFMTN) for multi-Pedestrian attribute recognition (DFMTN4PAR). DFMTN can perform pedestrian detection, pedestrian attribute recognition and pedestrian re-identification in one shot in a single neural network by adopting a multi-task learning strategy, boosted with the proposed dynamic fitting method to prevent gradient explosions. We have extensively evaluated the proposed DFMTN4PAR approach for its accuracy and performance, and compared with the state of art work, which shows that DFMTN4PAR can significantly improve the detection efficiency while ensuring accuracy, and compared with DeepMAR, the DFMTN4PAR approach improves the pedestrian attributes recognition accuracy by 5%-20%, and the detection speed by 30%-60%. The DFMTN4PAR approach has been successfully deployed to 10 smart residential areas in Qingdao, China and runs stably for over a year.
Machine Learning Approaches to Evaluating Quantum Phase Estimation Algorithm Output

Quantum phase estimation is one of the most fundamental and useful quantum computing algorithms available to researchers, but traditional methods for estimating the phase of a unitary operator acting on its eigenvalue leave information about the phase contained in all of the output from a quantum computer unused. Mean squared error (MSE) for such a scheme is 0.03 for simulated data with and without noise. Machine learning with classical methods, like linear regression (MSE=0.011 for noisy data and 0.0043 for noise-free data) and random forests (MSE=0.01 for noisy data and 0.006 for noise-free data) increase the accuracy of the estimation drastically without requiring additional qubits like the traditional approach. However, neural networks reduce this number further (MSE=0.0077) in the case of noisy data and with noise-free data (MSE=0.003). Examining the performance of neural networks with different modifications also yields insight into their performance on quantum computing output that could be generalized to output from other algorithms and on quantum computers with different numbers of qubits.
The Effective Breast Cancer Classification with the Random Forest Algorithm

Breast cancer is a serious threat to public health around the world. Breast cancer diagnosis and categorization are critical for efficient therapy and a higher likelihood of cure. The Random Forest algorithm was utilized to categorize breast cancers in this work, with the Breast Cancer Wisconsin (BCW) dataset serving as training data. The primary goal of this research is to create an effective breast cancers classification model utilizing the Random Forest method. Setting parameters and random forest configurations, such as the number of trees in the forest and random state values, were used in this study to get the best performance in breast cancer classification. The Random Forest with appropriate hyper parameters produced a highest accuracy of 99.12% in testing. This model is more accurate than earlier research in classifying breast cancers. This discovery helps to diagnose breast cancer and reduces misdiagnosis, enhancing the patient's chances of recovery.
Development of a Face Mask Type Detection with Multiclass Classification Using Artificial Intelligence on Python

This research presents a system based on a machine learning system with artificial intelligence. The approach presented uses a camera to detect people who are not wearing face masks and it is hoped that it can detect type from mask. The purpose of this study is to be able to compare whether a computer system can correctly distinguish different types and types of masks using multiclass classification with artificial intelligence and training datasets on the system. And testing with different amounts of data does it affect the detection sensitivity of the system. The two types of masks have a large accuracy value of 99%, and when the program is run with a multi-category dataset of four types of masks has a lower accuracy model rate of 93%. The main contributions of this research are creating a prototype face mask detector which is implemented in three phases to help detect the presence of a face mask detector in real-time using images and video streams. The model created has used deep-learning methods to develop classifiers and collect photos of individuals wearing masks and not wearing masks. Mask and non-mask class. Which is then implemented in Python with Google Colab environment along with the Open-CV modules, Keras, NumPy, tensor flow, sci-py, and matplotlib. Trials will be carried out with the dataset training process first using CNN and then collecting the data system accuracy.
Object Detection Using Vision Transformed EfficientDet

Computer vision, a subdivision of computer science and artificial intelligence focuses on enabling computers to interpret and analyze visual data from the world, such as images and videos. Recent advances in convolutional neural networks (CNNs), have improved the performance of computer vision systems remarkably, making them more accurate and efficient than ever before. Object detection using CNNs is a popular application of deep learning in computer vision. There are several popular frameworks for object detection that are widely used in the industry with many using the primary concept of convolution e.g., RetinaNet, EfficientNet, and EfficientDet etc. In this paper, we propose a novel hybrid approach for object detection by combining the power of Vision Transformers (ViT) with state-of-the-art EfficientDet architecture, resulting in a powerful object detection framework. The ViT backbone, known for its success in image classification and natural language processing (NLP) tasks, captures global dependencies in the input image using self-attention mechanisms. By incorporating ViT into the EfficientDet architecture, we enhance its ability to capture fine-grained details and context information, leading to improved object detection accuracy which leverages the strengths of both among other improvements to achieve highly accurate and efficient performance. The training of the model was done using PASCAL VOC 2007 and 2012 datasets and testing was executed on PASCAL VOC 2007 to achieve a mAP of 86.27%.
The Impact of Online Course Design, Enjoyment, Usefulness, Ease of Use, and Interaction to Video-Based Learning System Usage

Internet user has shown significant growth in Indonesia for the past year and because of that Video based learning is a growing popular learning option for most people to use. However efficiency and usefulness of such learning system has been questioned to having little to no impact in promoting teaching and learning. This research aims to answer and examine the impact of Online Course Design, Enjoyment, Usefulness, Ease of Use, and Interaction Video Based Learning System Usage in education. The data gathering methodology used for this research is Purposive sampling and data analysis on Smart-PLS. The data gathering platform utilize Google form, especially in the Jabodetabek area (Cities in Indonesia) region, from April until July 2023 to make sure respondents can provide data remotely. Tools used to analyze SEM is SMART PLS. The research model has eight variables in total. There are nine hypotheses in total in which eight are significant, and one is insignificant. Conclusion of the research showed that video based learning beneficial for education and is one of the most popular tools by young people to increase their general knowledge as well as promoting conduct of self learning by using video based learning but the research also found that it also resulted in the reduction of interaction to a state where there is little to interaction being present in the learning method.
Analysis and Discussion of the Issue

The analysis and discussion are collectively performed to emphasize the hermeneutical nature of this work’s development. Here, the knowledge base [3] must behold a rather dynamic than linear process nourishing the main ideas of this analysis. Instead of a strict division between analysis and discussion, both parts are intertwined to do justice to the complexity of each facet of this issue.
A Quantum Probabilistic Comparator: Circuit Implementation

Decision making in stochastic autonomous systems, which considers uncertainty of many factors, is always a challenge. The problem can typically be addressed by probabilistic methods such as approximate stochastic optimization or Monte Carlo (MC) simulation. The potential solution may usually be constrained by the system's computing power, due to the size of the probabilistic problem. This paper aims to present and implement a quantum probabilistic comparison (QPC) algorithm that can help to find a desired solution or make decision between two random variables under uncertainty. This approach takes advantage of quantum computing to perform parallel comparison and fast calculation of the probability of matched state vectors. In the presented scheme, two random variables have different values (e.g., 2, 4, 8, or more values) and different probability distributions. The data of the variables are encoded to the basis states of the corresponding qubits. The quantum probabilistic comparator is used to make up the quantum output matrix that stores the comparison solutions as well as their probability probabilities. Moreover, unlike the existing quantum comparator algorithm, which uses bit string comparison approaches, the proposed probabilistic comparator is not only comparing the basis state, but also accumulating the outcome's probabilities. The calculation results of the quantum probabilistic comparator circuit are compared with those from classical computation and quantum circuit simulation as well as on an IBM quantum computer, for analysis and verification.
A Study on the Role of Fringe Benefits on Employees Job Satisfaction

The purpose of this study was to determine the role of fringe benefits on employee job satisfaction. A descriptive research design was adopted for this research. The population of the study was employees of manufacturing industry. The researcher used convenient sampling technique to draw a sample size of 160 respondents. The data is collected using structured questionnaire developed by the researcher specifically for this study. A set of descriptive statistics including Bar chart and frequency tables were used to present the results of the study. The research result showed that fringe benefits as an incentive significantly affects employees morale, productivity and enhancing employees performance towards organizational tasks. This study helped to know about the role of fringe benefits that leads to improving employee job satisfaction in an organization and relationship between fringe benefits and job satisfaction. And finally that the future researchers should conduct more study on the issue of role of fringe benefits on employee job satisfaction in order to facilitate of this intricate concept.
Revolutionizing Sales with Phone Systems: Design and Implementation Best Practices

The technique of classifying a face as recognized or unknown in computer vision is known as face recognition. The procedure follows a pipeline that includes steps for data collection, detection, pre-processing, and recognition. This research focuses on with the assumption that photos have already been processed at the end of the pipeline pre-processed and gathered Face recognition software in the past has relied on the full face. Their algorithms take a face picture as input. We propose a new method in which the individual facial segments, such as the left, are sent into the recognition algorithm. The four components that make up the face are the left eye, the right eye, the nose, and the mouth. After that, the output of each algorithm is labelled, and it is categorized as belonging to right feature class. According to the findings of our research, the granularity of information gathered for each subdivision class has the potential to be utilized in order to create a higher accuracy rate than can be achieved by using the complete face technique.