Reviewerly/exHarmony huggingface dataset:
The God-I-Box: Iteratively Provotyping Technology-Mediated Worship Services

The COVID-19 pandemic accelerated the development of alternative formats for religious rituals, such as Protestant online worship services. However, current design approaches focus on problem-solving, and the resulting online solutions merely imitate the offline status quo. To overcome these limitations, we suggest adopting a provotype approach that allows for a more holistic, open-ended dialogue with those affected. We iteratively developed a first provotype in response to tensions found in observation-based field research, aiming to test whether and how it can trigger productive impulses for exploring future technology-mediated worship services based on existing experiences and perspectives. The resulting God-I-Box exaggerates individuality and allows congregants to act almost like liturgists. An analysis of congregants’ and pastors’ (online) first encounters with the God-I-Box revealed three reaction modes: spontaneous emotions, reflective coping, and exploratory imagination. We conclude with reflections and recommendations for provocative research and design in this context and beyond.
An orthodontic path planning method based on improved gray wolf optimization algorithm

Automatic tooth arrangement and path planning play an essential role in computer-aided orthodontic treatment. However, state-of-the-art methods have some shortcomings: low efficiency, excessive cost of displacements or collisions and insufficient accuracy. To address these issues, this paper proposes an innovative orthodontic path planning method based on the improved gray wolf optimization algorithm, which is called OPP-IGWO. First, the tooth model is preprocessed to obtain initial pose in which each tooth is segmented and built up with an oriented bounding box. Next, the target pose of each tooth is determined through the ideal dental arch curve and the optimal jaw principle. Finally, the path from the initial pose to the target pose of each tooth is planned based on IGWO, which is improved mainly from three aspects: (1) The greedy idea is adopted to initialize the gray wolf population based on dental interpolation. (2) The linear convergence factor in the traditional gray wolf optimization algorithm (GWO) is replaced with a nonlinear convergence factor. (3) We propose a position update strategy based on a dynamic weighting approach, which introduces a learning rate for each gray wolf. The experimental results show that our OPP-IGWO method outperforms the state-of-the-art methods. Compared with improved genetic algorithm, multiparticle improved swarm optimization, normal simplified mean particle swarm optimization algorithm, improved artificial bee colony algorithm, chaotic gray wolf optimization algorithm, hybrid gray wolf optimization algorithm and differential evolution algorithm, random walk gray wolf Optimization algorithm and reinforcement learning gray wolf optimization algorithm, the OPP-IGWO has an improvement on performance by 15.46%, 2.24%, 7.18%, 15.99%, 7.67%, 1.01%, 1.42%, 10.23%, respectively.
PENGARUH MEDIA E-COMIC MATERI ARITMATIKA SOSIAL TERHADAP MINAT BELAJAR SISWA SMP

The lack of interest in student learning can occur, one of which is because the teacher lacks variety in the use of media in the learning process in the classroom. This can happen because of the lack of knowledge about various media that can be used in learning. This study aims to describe the results of the application of e-comic media to social arithmetic material, and to determine the influence of the media on junior high school students' learning interest. This study uses a quantitative approach with the type of research that is pre-experimental with a one group pre test-post test design, which consists of the independent variable (e-comic social arithmetic material) and the dependent variable (student learning interest). The research sample was 43 students who had received social arithmetic material at SMP LKIA Pontianak. The data collection instrument is a questionnaire. Data were analyzed by descriptive analysis and inferential analysis. Based on the results of student responses, it was found that the application of e-comic media for social arithmetic material was classified as very good and the results of the hypothesis test stated that there was an effect of using e-comic media for social arithmetic material on students’ learning interest at SMP LKIA Pontianak.
A novel intrarenal multimodal 2D/3D registration algorithm and preliminary phantom study

Retrograde intrarenal surgery (RIRS) is a widely utilized diagnostic and therapeutic tool for multiple upper urinary tract pathologies. The image-guided navigation system can assist the surgeon to perform precise surgery by providing the relative position between the lesion and the instrument after the intraoperative image is registered with the preoperative model. However, due to the structural complexity and diversity of multi-branched organs such as kidneys, bronchi, etc., the consistency of the intensity distribution of virtual and real images will be challenged, which makes the classical pure intensity registration method prone to bias and random results in a wide search domain. In this paper, we propose a structural feature similarity-based method combined with a semantic style transfer network, which significantly improves the registration accuracy when the initial state deviation is obvious. Furthermore, multi-view constraints are introduced to compensate for the collapse of spatial depth information and improve the robustness of the algorithm. Experimental studies were conducted on two models generated from patient data to evaluate the performance of the method and competing algorithms. The proposed method obtains mean target error (mTRE) of 0.971 ± 0.585 mm and 1.266 ± 0.416 mm respectively, with better accuracy and robustness overall. Experimental results demonstrate that the proposed method has the potential to be applied to RIRS and extended to other organs with similar structures.
Symbiosis of an artificial neural network and models of biological neurons: Training and testing

In this paper, we show the possibility of creating and identifying the features of an artificial neural network (ANN), which consists of mathematical models of biological neurons. The FitzHugh–Nagumo (FHN) system is used as a paradigmatic model demonstrating basic neuron activities. First, in order to reveal how biological neurons can be embedded within an ANN, we train the ANN with nonlinear neurons to solve a basic image recognition problem with an MNIST database; next, we describe how FHN systems can be introduced into this trained ANN. After all, we show that an ANN with FHN systems inside can be successfully trained with improved accuracy comparing with first trained ANN and then with inserted FHN systems. This approach opens up great opportunities in terms of the direction of analog neural networks, in which artificial neurons can be replaced by more appropriate biological ones.
Comparison of Edge and Cloud Computing Technology for Industry 4.0 Perspective on the Future

As greater sophistication in technology and the Internet of Things become pervasive, a growing proportion of businesses recognize the value of integrating connected devices into their production processes. When an increasing number of gadgets are linked to an increasing number of networks, an increasing amount of data must be analyzed. However, as the generation of large data grows, so do the analysis and processing obstacles. Increasingly, evaluating and analysis issues are being solved using cloud computing technology. Nonetheless, there is a shift toward employing computational technologies to perform analysis as close to the device as is practically possible. This article, which examines the benefits and drawbacks of peripheral computing and cloud services and considers their potential applications within the context of Industry 4.0, was inspired by these considerations. Keywords: Internet of Things (IoT), the fourth industrial revolution, cloud computing technology, edge technology, and data analysis Process.
Research on the Classification Methods of Social Bots

In order to ensure the healthy development of social networks and the harmony and stability of the society, as well as to facilitate effective supervision by regulatory authorities, a classification method of social bots is proposed based on the identification of social bots in the early stage. First of all, the topic-related introduction is used to expand the topic, and on this basis, the SBERT (Sentence-BERT) model is applied to make relevance judgments between the micro-blog text and the expanded topics to identify polluters. Then, an opinion sentence recognition method that combines social bots opinion sentence generation rules with a deep learning model TextCNN is proposed to further distinguish commenters and spreaders. Finally, in order to improve the classification effect of the model, the transfer learning method is used to train the model with the help of a large number of micro-blogs of ordinary Weibo accounts, so as to better improve the classification effect of social bots. The comparative experimental results show that the topic expansion method can effectively improve the classification results of the SBERT model for the relevance of micro-blog text topics. When the parameter k of the expanded topic model is set at 20, the content of the expanded topic sequence is more consistent with the core content of most Weibo text sequences, and the obtained model has the best performance. By analyzing the opinion-based micro-blog text generation rules of social bots, focusing on the keywords that express opinions, the problem of difficulty in recognizing opinion sentences produced by the low quality of opinion sentences of social bots is well resolved, and the recognition effect of opinion sentences has been improved by more than 10%. Through the introduction of transfer learning, the problem of insufficient social bots data is effectively alleviated, and the classification effect of social bots is greatly improved, with an increase of more than 10%.
The Contribution of Selected Linguistic Markers for Unsupervised Arabic Verb Sense Disambiguation

Word sense disambiguation (WSD) is the task of automatically determining the meaning of a polysemous word in a specific context. Word sense induction is the unsupervised clustering of word usages in a different context to distinguish senses and perform unsupervised WSD. Most studies consider function words as stop words and delete them in the pre-processing step. However, function words can encode meaningful information that can help to improve the performance of WSD approaches. We propose in this work a novel approach to solve Arabic verb sense disambiguation that is based on a preposition-based classification that is used in an automatic word sense induction step to build sense inventories to disambiguate Arabic verbs. However, in the wake of the success of neural language models, recent works obtained encouraging results using BERT pre-trained models for English-language WSD approaches. Hence, we use contextualized word embeddings for an unsupervised Arabic WSD that is based on linguistic markers and uses sentence-BERT Transformer pre-trained models, which yields encouraging results that outperform other existing unsupervised neural AWSD approaches.
Security is Local: The Influence of the Immediate Workgroup on Information Security

Information security is a multilevel phenomenon with employee security decisions being influenced by macrolevel factors (e.g., organizational policies), mesolevel factors (e.g., one’s immediate workgroup—IW), and microlevel factors (e.g., individual personalities). We argue that an employee’s local IW (i.e., immediate supervisor and coworkers) has a strong effect on security. This paper focuses on the effects of these mesolevel factors in the presence of macro- and microlevel factors. Drawing on the social structure and social learning framework as well as workgroup research, we hypothesize that the security behavior of an employee’s IW supervisor and coworkers moderated by the nature of these relationships influences information security decisions. Our research, based on a sample of 217 full-time employees, reveals that the IW significantly affects security decisions, over and above the micro- and macrolevel factors. These effects are moderated by the nature of the relationship between employees and their IW supervisor (leader-member exchange) and coworkers (team-member exchange). A post hoc analysis shows that the mesolevel factors alone had the same explanatory power as the micro- and macrolevels combined. Our research suggests that future theory and research should include the IW and that organizations should share security responsibilities with line managers and help them understand their substantial impact on information security. Security training programs should ask employees about the behaviors of their IW supervisor and coworkers and, where needed, deliver anti-neutralization training to mitigate the effects of the IW’s noncompliance behaviors.
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Purpose of the study: The purpose of the study was to discuss the impact of the TOE factors on FinTech Adoption and Financial Performance and the role of organizational readiness as moderation in Jordanian commercial banks.Theoretical framework: In order to examine how to embrace Fintech and its effects on Financial Performance, this study provides an enhanced technology acceptance model (TOE) that includes relative advantage, top management support, competitive pressure, and technological compatibility. Method:A questionnaire that we created and distributed to bank managers, department heads, and supervisors working for Jordanian commercial banks yielded 215 valid replies.To test the hypotheses, we used a structural equation model (SEM) to analyze the data and examine the correlations between all latent variables. Results and conclusion:The results reveal that a positive and significant relationship between Relative Advantage, Top Management Support, and Competitive Pressure on Fintech Adoption and technological compatibility has an insignificant effect on Fintech Adoption, Also showed a positive and significant relationship between Fintech Adoption and Financial Performance.The significant moderating positive effect of organizational readiness on the relationship between Technological Compatibility and Competitive Pressure and Fintech Adoption.This study also shows that does not have a significant moderating positive effect of organizational readiness on the relationship between Relative Advantage, Top Management Support, and Fintech Adoption.Search implications: The study's findings imply the need for further research and exploration into FinTech Adoption to improve the financial performance of commercial banks to remain competitive in the market.Originality value: This research contributes to integrating the adoption of Fintech's trust with TOE, this study adds to the body of knowledge on the usage of Fintech in banks by offering a more thorough understanding of the factors that influence users' views.
3D Point-Voxel Correlation Fields for Scene Flow Estimation

In this paper, we propose Point-Voxel Correlation Fields to explore relations between two consecutive point clouds and estimate scene flow that represents 3D motions. Most existing works only consider local correlations, which are able to handle small movements but fail when there are large displacements. Therefore, it is essential to introduce all-pair correlation volumes that are free from local neighbor restrictions and cover both short- and long-term dependencies. However, it is challenging to efficiently extract correlation features from all-pairs fields in the 3D space, given the irregular and unordered nature of point clouds. To tackle this problem, we present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">point-voxel correlation fields</b> , proposing distinct point and voxel branches to inquire about local and long-range correlations from all-pair fields respectively. To exploit point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region, which guarantees the scene flow estimation precision. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences, which are utilized to handle fast-moving objects. Integrating these two types of correlations, we propose Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) architecture that employs an iterative scheme to estimate scene flow from point clouds. To adapt to different flow scope conditions and obtain more fine-grained results, we further propose Deformable PV-RAFT (DPV-RAFT), where the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Spatial Deformation</b> deforms the voxelized neighborhood, and the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Temporal Deformation</b> controls the iterative update process. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets and experimental results show that we outperform state-of-the-art methods by remarkable margins. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/weiyithu/PV-RAFT</uri> .
Solving MaxCut with quantum imaginary time evolution

We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state involving no entanglement, as well as an imaginary-time-dependent Hamiltonian interpolating between a given graph and a subgraph with two edges excised. We apply the method to thousands of randomly selected graphs with up to fifty vertices. We show that our algorithm exhibits a 93% and above performance converging to the maximum solution of the MaxCut problem for all considered graphs. Our results compare favorably with the performance of classical algorithms, such as the greedy and Goemans–Williamson algorithms. We also discuss the overlap of the final state of the QITE algorithm with the ground state as a performance metric, which is a quantum feature not shared by other classical algorithms.
BLOCKCHAIN TECHNOLOGY AND APPLICATION – SMEs ASPECT

&lt;em&gt;This review article is designed for SME owners and directors to understand better what blockchains are and how they work and to support them to start an entirely new blockchain or fork an existing one and create a new token. The article presents technology bases and how blockchains can serve SMEs and foreseeable challenges that could jeopardize the achieved results. Particular attention the authors paid to blockchain applications, transaction cost reduction, and sensitive business information security. &lt;em&gt;Even more attention the authors gave to the challenges&lt;/em&gt; connected with digital infrastructures and business practices, investments, lack of interoperability, lack of skills, and legal aspects. &lt;em&gt;Chapter four discusses the creation of blockchains for SMEs &lt;/em&gt;from the aspects of choosing a type of blockchain, choosing of consensus mechanism, a platform selection, nodes design, operating systems, instances design, application programming interfaces, and user interfaces, and the blockchain finalizing. In chapter five, Conclusions, the authors draw conclusions based on the work presented in the article.&lt;/em&gt;
Analysis-Ready Data and FAIR-AI—Standardization of Research Collaboration and Transparency Across Earth-Observation Communities [Technical Committees]

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
The Relationship Between Transformational Leadership and Public Sector Employees Job Satisfaction During COVID-19

Purpose: The study intends to assess the effect of transformational leadership on the job satisfaction of government employees in Jordan in order to further investigate this phenomenon. Theoretical framework: Worldwide public sector companies were facing numerous operational difficulties as a result of the Corona pandemic. Among the most important steps implemented to prevent viral spread are lockdowns and social seclusion. However, strategic management and leadership can support overcoming existing difficulties. Design/methodology/approach: Using an acceptable sample technique, the researchers chose the respondents from among human resource staffers working for public sector organizations. Additionally, regression analysis supported the suggested association between the study variables. Findings: The findings showed a significant positive association between transformative leadership and employees' happiness with their jobs in human resources. Research, Practical &amp; Social implications: It has been discovered that, despite the COVID-19 epidemic, transformational leaders support their teams' productivity, which raises commitment to the organization and employee job satisfaction. Originality/value: Regarding the function of transformative leaders, participants also expressed satisfaction and confidence. The researchers advised undertaking additional research to emphasize the importance of leadership in strategically implementing administrative procedures amid global crises at all levels.
DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version

Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success, however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes average rewards, which may disregard rare but critical situations and hence lack local robustness; (ii) optimization objectives targeting safety typically yield degenerated reward structures, which, for DRL to work, must be replaced with proxy objectives. Here, we introduce a methodology that can help to address both deficiencies. We incorporate evaluation stages (ES) into DRL, leveraging recent work on deep statistical model checking (DSMC), which verifies NN policies in Markov decision processes. Our ES apply DSMC at regular intervals to determine state space regions with weak performance. We adapt the subsequent DRL training priorities based on the outcome, (i) focusing DRL on critical situations and (ii) allowing to foster arbitrary objectives. We run case studies on two benchmarks. One of them is the Racetrack, an abstraction of autonomous driving that requires navigating a map without crashing into a wall. The other is MiniGrid, a widely used benchmark in the AI community. Our results show that DSMC-based ES can significantly improve both (i) and (ii).