Reviewerly/exHarmony huggingface dataset:
Game development software engineering: digital educational game promoting algorithmic thinking

&lt;span&gt;The purpose of this study is to create a digital educational game that promotes algorithmic thinking for elementary school students. However, the processes of game development differ from traditional software development which cannot guarantee its effectiveness in terms of human-machine interfaces. In this article, we propose a new game development software engineering (GDSE) as a model for game development. This new model aims to complement and mitigate the shortcomings of traditional software development. The principles of human-computer interaction are now incorporated in the new model. The GDSE includes design, development, usability inspection, game experience evaluation, educational value evaluation and release. It was used as a research method to develop a game that promotes algorithmic thinking for children. The results of this study are not only a digital educational game that promotes algorithmic thinking for children but also a new game development life cycle that guarantees the performance of digital games in terms of usability enhancement, game experience and educational value.&lt;/span&gt;
An approach based on deep learning that recommends fertilizers and pesticides for agriculture recommendation

&lt;span&gt;With the advancement of the internet, individuals are becoming more reliant on online applications to meet most of their needs. In the meantime, they have very little spare time to devote to the selection and decision-making process. As a result, the need for recommender systems to help tackle this problem is expanding. Recommender systems successfully provide consumers with individualized recommendations on a variety of goods, simplifying their duties. The goal of this research is to create a recommender system for farmers based on tree data structures. Recommender system has become interesting research by simplifying and saving time in the decision-making process of users. We conducted although a lot of research in various fields, there are insufficient in the agriculture sector. This issue is more necessary for farmers in Quangnam-Danang or all Vietnam countries by severe climate features. Storm from that, this research designs a system based on tree data structures. The proposed model combines the you only look once (YOLO) algorithm in a convolutional neural network (CNN) model with a similarity tree in computing similarity. By experiments on 400 samples and evaluating precision, accuracy, and the value of the predictive test as determined by its positive predictive value (PPV), the research proves that the proposed model is feasible and gain better results compared with other state-of-the-art models.&lt;/span&gt;
A novel method for predicting the geochemical composition of tailings with laboratory field and hyperspectral airborne data using a regression and classification-based approach

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Enhancing Remote Industrial Training Experiences with Asymmetric Virtual Reality: Experiences, Tools and Guidelines

Training in virtual reality (VR) is a valuable supplementing tool for advancing knowledge transfer that results in increased efficiency and accuracy of technicians in fieldwork. However, COVID-19 pandemic restrictions made it impossible for VR training centers to operate on a full scale, forcing traditional face-to-face learning sessions to become remote. In this article, we investigate the asymmetric use of a VR training solution—among devices with different levels of immersion and control—to enrich the content of remote training sessions. The VR in this case can be seen as a source of visual and other contextual information to advance the effects of situated learning and enhance knowledge transfer. To evaluate this approach, we conducted a remote user study with ten industrial maintenance and installation experts. We also introduce the “Research Panel” tool to gather reactions of learners during the remote training session. The expert user study results demonstrate the usefulness and relevance of asymmetric VR to improve remote training sessions and other application industrial scenarios, while the “Research Panel” data provided detailed insight into the session flow. Building on the qualitative findings, we present design guidelines to aid the adoption of asymmetric VR in the industrial context.
Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations

Abstract This article combines the fractional differential theory with the total variation method and applies it to defining cultural industry image color matching. At the same time, we propose a new image color matching denoising model based on fractional partial differential equations. The model achieves simultaneous denoising in the time direction and the spaceplane. Experiments have proved that the fractional partial differential equations method has more advantages than integer-order partial differential equations in denoising and reducing step effects. It can effectively improve the contrast and clarity of image color matching in the cultural industry.
Quixo is Solved

Quixo is a two-player game played on a $$5\times 5$$ grid where the players try to align five identical symbols. Using a combination of value iteration and backward induction, we propose the first complete analysis of the game. We describe memory-efficient data structures and algorithmic optimizations that make the game solvable within reasonable time and space constraints. Our main conclusion is that Quixo is a Draw game. The paper also contains the analysis of smaller boards and presents some interesting states extracted from our computations.
Extractive Text-Image Summarization with Relation-Enhanced Graph Attention Network

Abstract Multi-modal summarization with multi-modal output (MSMO) aims to generate multi-modal summaries for a multi-modal document to improve readability of summaries by making use of information of different modalities. Most existing Seq2Seq-based MSMO models cannot well capture multi-modal relations which are significant for generating high-quality multi-modal summaries. To address this issue, this paper proposes a relation-enhanced graph attention network for extractive text-image summarization (ReGAT-Summ) to capture inter-modal and intra-modal relations in the multi-modal document. Firstly, a multi-modal graph is constructed from the document. Then, node representations are calculated by proposed graph neural network. Finally, a sentence-image selector is trained to select salient sentences and images, which are further aligned by training. To our knowledge, we are the first to explore the graph-based model for MSMO. Experiments on two news datasets E-DailyMail and NYTime800k demonstrate that ReGAT-Summ achieves the state-of-the-art performance in terms of automatic metrics and human evaluations.
Designing Socio-Technical Systems Using the System Paradigm in the Context of Nano-, Bio-, Information Technology and Cognitive Science Convergence

Purpose: The purpose of the study is to develop a methodology for designing socio-technical systems using the system paradigm in the context of nano-, bio-, information technology and cognitive science convergence. Methodology/Approach: The systemic paradigm is used. The optimization is carried out according to the integral indicator of resource consumption and system energy efficiency. Fractal socio-technical systems are created that provide the maximum correlation between the needs of individuals and the activities of society, taking into account the dynamics of the formation of needs for the purpose of self-development with restrictions on safety factors and material resources. Findings: The proposed methodology makes it possible to develop socio-technical systems with a high level of security, ensuring sustainable spiral self-development and integration of scientific knowledge on the basis of adaptive, innovative, intuitive and analytical elements of the system. Research Limitation/Implication: A general concept for designing socio-technical systems using the system paradigm in the context of nano-, bio-, information technology and cognitive science convergence is presented that requires further research. Originality/Value of paper: A methodology for designing socio-technical systems using the system paradigm in the context of nano-, bio-, information technology and cognitive science convergence was first proposed.
Unfolding Values through Systematic Guidance: Conducting a Value-Centered Participatory Workshop for a Patient-Oriented Data Donation

Routinely collected clinical patient data posits a valuable resource for data-driven medical innovation. Such secondary data use for medical research purposes is dependent on the patient’s consent. To gain an understanding of the patients’ values and needs regarding medical data donations, we developed a participatory workshop method, integrating approaches from value-sensitive and reflective design to explore patients’ values and translate them into hypothetical, ideal design solutions. The data gathered in the workshop are used to derive practicable design requirements for patient-oriented data donation technologies. In this paper, we introduce the workshop process and evaluate its application.
Evaluation Model of Innovation and Entrepreneurship Ability of Colleges and Universities Based on Improved BP Neural Network

Entrepreneurship education activities in colleges and universities play an important role in improving students’ innovation ability. Therefore, this paper has important practical value to evaluate the innovation and entrepreneurship ability of college students. At present, most studies use qualitative research methods, which is inefficient. Even if quantitative analysis is adopted, it is mostly linear analysis, which is inconsistent with the actual situation. In order to improve the application level of genetic algorithm to the innovation and entrepreneurship ability of universities based on BP neural network, this paper studies the evaluation model of innovation and entrepreneurship ability of universities. Based on the simple analysis of the current situation of university innovation and entrepreneurship ability evaluation and the application progress of BP neural network, combined with the actual situation of university innovation and entrepreneurship, this paper constructs the innovation and entrepreneurship evaluation index, uses BP neural network to build the evaluation model, and uses genetic algorithm to optimize and improve the shortcomings of BP neural network. Then, the experimental analysis and application design are carried out. The results show that the improved algorithm is basically consistent with the predicted value, small error, and fast convergence. When it is used in the evaluation of innovation and entrepreneurship ability, quantitative analysis results can be obtained, which provides a certain reference for the development of enterprises.
Donating Health Data to Research: Influential Characteristics of Individuals Engaging in Self-Tracking

Health self-tracking is an ongoing trend as software and hardware evolve, making the collection of personal data not only fun for users but also increasingly interesting for public health research. In a quantitative approach we studied German health self-trackers (N = 919) for differences in their data disclosure behavior by comparing data showing and sharing behavior among peers and their willingness to donate data to research. In addition, we examined user characteristics that may positively influence willingness to make the self-tracked data available to research and propose a framework for structuring research related to self-measurement. Results show that users’ willingness to disclose data as a “donation” more than doubled compared to their “sharing” behavior (willingness to donate = 4.5/10; sharing frequency = 2.09/10). Younger men (up to 34 years), who record their vital signs daily, are less concerned about privacy, regularly donate money, and share their data with third parties because they want to receive feedback, are most likely to donate data to research and are thus a promising target audience for health data donation appeals. The paper adds to qualitative accounts of self-tracking but also engages with discussions around data sharing and privacy.
A proposal to enhance data quality and <scp>FAIRness</scp>

Abstract In recent years, we witnessed an increasing number of funding agencies, scientific journals and scientists agreeing that society and science benefit from open access to research data. Benefits derive mainly from increased access to knowledge for all and improved transparency and credibility in academia. However, despite the advances in open science and open data, three significant aspects still need considerable policing: data quality, the accompanying summaries with basic information of the data files (i.e. metadata) and computational codes used to generate the research outcomes. Only by having these three components together, we can achieve efficient data sharing and reuse, and hence higher transparency. Here, we present two complementary approaches that potentially can help with shared data quality: (i) data file(s) sharing should be guided step‐by‐step in public archives with mandatory metadata, and (ii) journals creating assistant data editor positions at editorial boards with a leading role in data quality and computational reproducibility. Forty‐four editors‐in‐chief in the field of behaviour, ecology and evolution shared their opinion with us regarding these two approaches. Although most of the views were divided, the majority estimated that their current editorial board members do not have the necessary skills to assess the quality of shared data. Since data are the core of research studies, we should consider not only data presence but also quality as a requirement for publication.
Contributions of Quantum Factoring on Quantum Research

This installment of Computer’s series highlights work published in IEEE Symposium on Foundations of Computer Science. Although we typically highlight articles from work published in IEEE Computer Society journals, this month’s exception is due to the prevalence and significance of quantum computing research progress in recent years.
EFL Slow Learners’ Perception in Speaking with Authentic Multimedia Assisted Language Learning

The proliferation of assisted technology has been widely used in language learning, but this should be able to accommodate inclusive language learning which has become an important issue in today’s English language teaching and the higher education landscape in Indonesia. This study examined the perception of EFL slow learners in terms of the integration of an inclusive technology with authentic multimedia assisted language learning (AMALL). It discovered the impact of authentic multimedia assisted language learning on their speaking performance. This study also analysed the benefits of this technology perceived by the students. A mixed-method sequential exploratory was employed to examine students’ perception of EFL slow learners in speaking performance. A sample of 30 EFL learners was selected purposively to participate in this study. The study also examined the impact of this application by comparing the results of pre-test and post-test of the students’ speaking performance based on the analytic oral language-speaking rubric namely speaking, fluency, structure, and vocabulary. The findings of the study show that students perceived authentic multimedia assisted language learning provides some advantages in terms of usefulness, easiness, and effectiveness for speaking skill. There was also an improvement in speaking performance
Stable parallel training of Wasserstein conditional generative adversarial neural networks

We propose a stable, parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs) under the constraint of a fixed computational budget. Differently from previous distributed GANs training techniques, our approach avoids inter-process communications, reduces the risk of mode collapse and enhances scalability by using multiple generators, each one of them concurrently trained on a single data label. The use of the Wasserstein metric also reduces the risk of cycling by stabilizing the training of each generator. We illustrate the approach on the CIFAR10, CIFAR100, and ImageNet1k datasets, three standard benchmark image datasets, maintaining the original resolution of the images for each dataset. Performance is assessed in terms of scalability and final accuracy within a limited fixed computational time and computational resources. To measure accuracy, we use the inception score, the Frechet inception distance, and image quality. An improvement in inception score and Frechet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks (DC-CGANs) as well as an improvement of image quality of the new images created by the GANs approach. Weak scaling is attained on both datasets using up to 2,000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.
NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering

Attributed graph clustering is one of the most fundamental tasks among graph learning field, the goal of which is to group nodes with similar representations into the same cluster without human annotations. Recent studies based on graph contrastive learning method have achieved remarkable results when exploit graph-structured data. However, most existing methods 1) do not directly address the clustering task, since the representation learning and clustering process are separated; 2) depend too much on data augmentation, which greatly limits the capability of contrastive learning; 3) ignore the contrastive message for clustering tasks, which adversely degenerate the clustering results. In this paper, we propose a Neighborhood Contrast Framework for Attributed Graph Clustering, namely NCAGC, seeking for conquering the aforementioned limitations. Specifically, by leveraging the Neighborhood Contrast Module, the representation of neighbor nodes will be 'push closer' and become clustering-oriented with the neighborhood contrast loss. Moreover, a Contrastive Self-Expression Module is built by minimizing the node representation before and after the self-expression layer to constraint the learning of self-expression matrix. All the modules of NCAGC are optimized in a unified framework, so the learned node representation contains clustering-oriented messages. Extensive experimental results on four attributed graph datasets demonstrate the promising performance of NCAGC compared with 16 state-of-the-art clustering methods. The code is available at https://github.com/wangtong627/NCAGC.
Probabilistic causal reasoning under time pressure

Causal reasoning is a core facet of our cognitive abilities. However, the time-course of causal reasoning is not studied to its fullest. The duration of reasoning, including those that yield a reasoning error, might prove crucial in understanding the cognitive processes underlying causal reasoning. In two experiments we asked participants to make probabilistic causal inferences while manipulating external time pressure and measuring response times. We found that participants are less accurate under time pressure, which can be interpreted as a speed-accuracy-tradeoff. In addition, we found that conservative responding increased under time pressure. Surprisingly, we found that two other persistent reasoning errors - Markov violations and failures to explain away - appear insensitive to the time pressure we employed. These observations seemed related to the confidence that participants expressed in their causal judgment: Conservatism was associated with low confidence, whereas Markov violations and failures to explain were not. These findings shed doubt on existing explanations of causal reasoning that predict an association between time pressure and all reasoning errors. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize the need for an understanding of causal judgements as the result of multiple processes.
Long-distance quantum teleportation from a photonic telecom qubit to a matter qubit

We demonstrate long-distance quantum teleportation from a telecom photonic time-bin qubit to a solid-state quantum memory. The storage time of the quantum memory allows for active feedforward, therefore completing the protocol.
EOLANG and $φ$-calculus

Object-oriented programming (OOP) is one of the most popular paradigms used for building software systems. However, despite its industrial and academic popularity, OOP is still missing a formal apparatus similar to $\lambda$-calculus, which functional programming is based on. There were a number of attempts to formalize OOP, but none of them managed to cover all the features available in modern OO programming languages, such as C++ or Java. We have made yet another attempt and created $\varphi$-calculus. We also created EOLANG (also called EO), an experimental programming language based on $\varphi$-calculus.
Fast Planner for MAV Navigation in Unknown Environments Based on Adaptive Search of Safe Look-Ahead Poses

Autonomous navigation capability is a crucial part for deploying robots in an unknown environment. In this article a reactive local planner for autonomous and safe navigation in subterranean environment is presented. The proposed planning framework navigates the MAV forward in a tunnel such that the MAV gains more information about the environment while avoiding obstacles. The proposed planning architecture works solely based on the information of local surrounding of the MAV thus, making navigation simple yet fast. One of the biggest novelties of the article comes from solving the combined problem of autonomous navigation and obstacle avoidance. The proposed algorithm for selecting the next way point of interest also accounts in the safety margin for traversing to such way point. The approach presented in this article is also different from classical map based global planning algorithms because it favours the next way point away from obstacles in way point selection process and thus providing a safe path for incremental forward navigation. The approach is validated by simulating a MAV equipped with the proposed reactive local planner in order for the MAV to navigate in a subterranean cave environment.
Sensitivity Analysis of MaskCycleGAN based Voice Conversion for Enhancing Cleft Lip and Palate Speech Recognition

Cleft lip and palate speech (CLP) is a congenital disorder which deforms the speech of an individual. As a result their speech is not amenable to the speech recognition systems. The existing work on CLP speech enhancement is by using CycleGAN-VC based non-parallel voice conversion method. However, CycleGAN-VC cannot capture the time-frequency structures which can be done by MaskCycleGAN-VC by application of a module named as time-frequency adaptive normalization. It also has the added advantage of mel-spectrogram conversion rather than mel-spectrum conversion. This voice conversion of a CLP speech to a normal speech increases the intelligibility and thereby allows automatic speech recognition systems to predict the uttered sentences which is necessary in day to day life as speech recognition devices are automatizing living on a large scale. But in order to develop an assistive technology it is very essential to study the sensitivity of automatic speech recognizers. This work focuses on the sensitivity analysis of a MaskCycleGAN based voice conversion system depending on the variation of acoustic and gender mismatch.