Reviewerly/exHarmony huggingface dataset:
Uncertainty‐aware iterative learning for noisy‐labeled medical image segmentation

Abstract Medical image segmentation from noisy labels is an important task since obtaining high‐quality annotations is extremely difficult and expensive. There are a lot of approaches proposed for such task. However, some issues like the overfitting on noisy annotations, the limited learning of boundary features, and no consideration of the corrupted local pixels are still not solved. Therefore, a novel approach named uncertainty‐aware iterative learning (UaIL) is proposed for medical image segmentation with noisy labels. UaIL iteratively and jointly trains two deep networks using the original images and their argumented ones through a joint loss function including softened label loss, hard label loss and consistency loss, which encourages UaIL to produce segmentations that are robust to the perturbations in arbitrary semantic space. The uncertainty of labels is estimated based on the predictions in iterative learning, then the original labels are refined, which improves the learning of boundary features in segmentation. To avoid overfitting, a stopping strategy is designed based on the dice coefficient in iterative learning. Experiments on two public datasets verify the effectiveness of UaIL under different levels of annotation noise. Especially, when there are serious noises in the labels, the dice achieved by UaIL is 1.43% to 15.03% higher than the competing approaches on the two public datasets. The UaIL is further verified on a private dataset, which shows its ability of applying in the real application with noisy labels.
The self, its body and its brain

The body is intrinsic to our sense of self and as such, any theoretical account of the self should also include contributions of the body. This Collection incorporates a series of papers that demonstrate the inextricable relationship between body and self. The papers include studies of body illusions and studies of observed differences in bodily experience in participants with psychiatric and physical conditions. Papers in the Collection also address methodological issues, because measuring and manipulating the bodily self does not come without challenges; subjective experiences are difficult to capture empirically. Making progress on these methodological limitations is crucial to further develop experimental design and thus our understanding of self-body relations.
Development of Scientific-Based Thematic Worksheets to Improve Cognitive and Language Abilities of Children Aged 5-6 Years

This study introduces a novel approach by utilizing distinct thematic worksheets with a scientific framework to actively engage 5-6-year-old children in critical thinking through the scientific process. The worksheets appreciate cognitive development, analytical skills, and higher-order thinking. This innovative method encourages active participation and the development of cognitive and analytical abilities in children. The research focuses on the development of scientific-based thematic worksheets to enhance the cognitive and language abilities of 5-6-year-old children in PAUD, specifically at the Soleh Children Education Park (TAPAS) in Taman District, Sidoarjo Regency. The study employed a quantitative approach with a one-group pretest-posttest design, involving 21 children as research participants. The development process followed the Research and Development (R&amp;D) framework, comprising 10 stages. Data collection included the validation of the worksheets by subject matter and media experts to assess their feasibility. The findings highlight the effectiveness of the scientific-based thematic worksheets, as demonstrated by significant improvements in the cognitive and language abilities of the 5-6-year-old students at TAPAS. Overall, this study contributes to early childhood education by introducing an innovative educational resource that enhances critical thinking skills and promotes deeper understanding among young learners.
NONLINEAR CLUSTERED ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM MODEL FOR HOURLY SOLAR IRRADIATION ESTIMATION

Solar energy occupies an important place among the various sources of renewable energy. A precise knowledge of the distribution of solar irradiation in a specified location is needed before any solar irradiation system installation. This paper introduces a nonlinear clustering, adaptive-network-based fuzzy inference system (ANFIS) model to estimate the hourly solar irradiation data using meteorological inputs and clustering algorithms: grid partitioning, subtractive clustering, and fuzzy c-means. Comparing these clustering algorithms is investigated to classify the inputs into clusters, which helps the solar irradiation estimation model build better. This method's advantage is understanding and simplifying the nonlinearity presented in the input’s datasets. Moreover, the FCM algorithm gives the best results from comparing the testing data; the RMSE is 43.2274 W/m2, and MSE equals 2001.34 W/m2 with an R2 equal to 0.9893.
Digitalisation, Safety and Privacy

Abstract In order to increase the safety of industrial facilities and people, firms and their managers traditionally pay attention to the visibility of activities and the intentions of workers. Firms and managers can use connected objects worn by workers to collect this data. Analysing the introduction of smart glasses and smart shoes in an industrial site, this contribution explains how workers can use these tools without sacrificing their autonomy and privacy. In this site, performance as well as the safety of the activities is based on a combination of high individual autonomy and solidarity between colleagues strengthened by a private life at work. The sociotechnical context of this industrial site and the desire of professionals to control their privacy at work have a strong impact on the trajectory of these technologies. They insist on the use of technologies with their colleagues which strengthen the bonds of cooperation and solidarity and are resistant to technologies that could geolocate them or trace their movements. Moreover, the association of user spokespersons is an essential condition for the success of the design and dissemination of digital technologies. Finally, the control of privacy and private life at work by the workers contributes to the reinforcement of the performance and the safety of production.
Information Security Behaviour in an Organisation Providing Critical Infrastructure: A Pre-post Study of Efforts to Improve Information Security Culture

Abstract The study examines whether information security behaviour (ISB) in an organisation providing critical infrastructure improved after systematic efforts to improve information security culture (ISC) through the implementation of an information security management system (ISMS). The data are based on quantitative surveys before ( N = 323) and after ( N = 446) efforts to improve ISC in the organisation. Qualitative interviews were also conducted before ( N = 22) and after ( N = 12). The study finds that the organisation has managed to improve its ISC through systematic efforts over a two-year period (2014–2016), and that this also has led to improvements in ISB among the personnel in the organisation. Multivariate regression analyses indicate that ISC is the most important variable influencing ISB, while ISMS measures is the most important variables influencing ISC. Thus, our results indicate that it is important to work with ISMS and ISC to increase IS in our increasingly digitalised society, especially in organisations providing critical infrastructure.
Analysis of Using Literacy Learning Web in Elementary School: Survey of Elementary School Teachers in Pekanbaru

Currently, technology plays a crucial role in various aspects, including education. Therefore, this study aims to provide an overview of the use of web-based technology in literacy learning among teachers in elementary schools. The research methodology employed a quantitative approach through a survey involving 55 teachers. The findings reveal that 74.55% of teachers have received training in learning technology, leading to 92.73% of them implementing technology-enhanced learning in schools. Particularly during the pandemic, teachers have been compelled to rely on learning technology and develop interactive learning media. However, it is important to note that many teachers still lack proficiency in utilizing technology, as only 47.27% of them are able to create web media or blogs as learning tools. The survey results also indicate that 74.55% of teachers face difficulties in creating online learning materials, while 65.45% struggle with teaching online. Surprisingly, only 1.3% of teachers reported using technology or the web in literacy learning, while a significant majority (83.6%) have never utilized it. Additionally, 3.6% of teachers expressed unfamiliarity with web-based technology. These findings suggest that the integration of web-based technology in literacy learning is still limited, possibly due to age-related constraints and a lack of expertise in the IT field among teachers.
Recording of Research Results in the Field of <i>Pantun</i> Writing Learning in Indonesia

This study aims to map article content, authorship patterns, characteristics, and productivity of researchers in the field of learning to write pantun in accordance with bibliometric studies. Article mapping is visualized through VOS viewer software. The period observed for mapping articles over the last ten years, from 2012 to 2022. The article data was obtained according to the distribution of articles identified through Google Scholar. Data analysis begins with (a) calculating the content of articles in a descriptive bibliometric manner, (b) analyzing the growth of research each year as a contribution to the field of teaching, (c) looking at the distribution of articles according to the type of language and author, (d) identifying authorship and calculating the level of collaboration between them, region and/or between universities, (e) observing the characteristics of the emergence of the main author and the second/next author based on geographical and institutional aspects, (f) analyzing the productivity and distribution of research results according to insight aspirations. The resulting data shows that the content of articles on learning to write pantun is mostly written for learning in primary and secondary schools. Articles are published in both National Conference, International Conference, and National Journals. The types of research are classroom research, experimental research, and development research.
Lost in Translation: The Limits of Explainability in AI

As artificial intelligence becomes more prevalent, regulators are increasingly turning to legal measures, like “a right to explanation” to protect against potential risks raised by AI systems. However, are eXplainable AI (XAI) tools - the artificial intelligence tools that provide such explanations – up for the task? This paper critically examines XAI’s potential to facilitate the right to explanation by applying the prism of explanation’s role in law to different stakeholders. Inspecting the underlying functions of reason-giving reveals different objectives for each of the stakeholders involved. From the perspective of a decision-subject, reason-giving facilitates due process and acknowledges human agency. From a decision-maker’s perspective, reason-giving contributes to improving the quality of the decisions themselves. From an ecosystem perspective, reason-giving may strengthen the authority of the decision-making system toward different stakeholders by promoting accountability and legitimacy, and by providing better guidance. Applying this analytical framework to XAI’s generated explanations reveals that XAI fails to fulfill the underlying objectives of the right to explanation from the perspective of both the decision-subject and the decision-maker. In contrast, XAI is found to be extremely well-suited to fulfil the underlying functions of reason-giving from an ecosystems’ perspective, namely, strengthening the authority of the decision-making system. However, lacking all other virtues, this isolated ability may be misused or abused, eventually harming XAI’s intended human audience. The disparity between human decision-making and automated decisions makes XAI an insufficient and even a risky tool, rather than serving as a guardian of human rights. After conducting a rigorous analysis of these ramifications, this paper concludes by urging regulators and the XAI community to reconsider the pursuit of explainability and the right to explanation of AI systems.
The possibilities of using mobile technologies in geometry lessons in school

The article presents the author's model of the use of mobile technologies in the study of geometry in basic school. An overview of online services frequently used in educational practice is given, examples of their use at different stages of mastering the content of the geometry course of the basic school are given.
Interlibrary Loan Use among Staff Members at a Small Liberal Arts College

AbstractThe object of this study is to assess why staff at Lyon College, of Batesville, Arkansas, utilize the Mabee-Simpson Library and its interlibrary loan service at a disproportionately lower rate than faculty, students, and visitors, a fact revealed through a prior analysis of interlibrary loan statistics. To determine the cause for this imbalance, a survey was created and emailed to staff. After they were permitted a month to participate in the survey, the instrument was closed and analyzed. Results from that analysis suggest a large percentage of respondents did not know they could use the Library, and an even larger percentage of respondents failed to realize they could use the Mabee-Simpson Library’s interlibrary loan service. These results are unsettling since staff total roughly 66% of all employees at Lyon College. Also, staff enjoy the same privileges as faculty, students, and visitors. In addition, several staff members are enrolled in higher education courses that require reading materials for class. The results of this study indicate the need for the Library to better promote itself and its interlibrary loan service to staff.Keywords: Interlibrary loan departmentsresource sharing Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsLaren AndersonLaren Anderson is the Resource Sharing Librarian at the Mabee-Simpson Library, within Lyon College, Batesville, AR.
Understanding the quality and evolution of Android app build systems

Abstract Build systems are used to transform static source code into executable software. They play a crucial role in modern software development and maintenance. As such, much research effort has been invested in understanding the quality and evolution of build systems, including Apache ANT, Apache Maven, and Make‐based ones. However, the quality and evolution of build systems for mobile apps, such as on the Android platform, have not as yet been investigated in detail. Mobile app development, and the Android development context in particular, impose unique constrains, such as different device conditions and capabilities. It presents unique challenges, such as frequently upgraded Android frameworks, which those who implement and maintain build systems must tackle. In this paper, we present an exploratory empirical study of the build systems of 5222 Android projects to better understand their quality and evolution. We (a) study the build technology choices that Android developers make (Gradle being recommended and the most popular choice), (b) explore the sustainability of the official Gradle build system (parts of build files are updated more frequent that others and the update of the special Gradle plugin would induce unrecommended configurations), and (c) analyze the quality of Gradle scripts for Android apps—more than a half of the open‐source Android apps cannot be successfully built due to five common root causes.
A systematic literature review of virtual embodied experience

Abstract Virtual scenarios have brought intriguing enhancements to the in‐reality experiences of individuals, and a paradigm of embodiment, rather than disembodiment, has evolved. Research on virtual embodied experience has connected the trends of virtualization and embodiment of experience but remains scattered and inconclusive. This systematic review of 125 peer‐reviewed journal papers in the Scopus database uncovers extant work on the contextualization, conceptualization, and causal links of virtual embodied experience. It finds that extant literature neglects certain cutting‐edge scenarios. Research still needs to identify more explicit dimensional structures of virtual embodied experience and more antecedents and consequences along the causal chain. Research has grounded itself in theoretical constellations across cognitive and behavioral science, sociology, and computer science. Nevertheless, flaws remain, not least the use of disembodiment‐oriented theories to explain embodied experience and insufficient emphasis on the subjectivity of technology. As the lack of suitable research frameworks has presented obstacles to quantitative research, this study proposes a framework highlighting virtual embodied experience bridging virtual scenario elements (technological and environmental factors) and multiple consequences (perception, responses, and performances). This study sets out a future research agenda, including promising research directions such as exploring virtual embodied experiences in multiple virtual scenarios, clarifying its dimensional structures, and underlining the complexity of how it mediates virtual scenario elements and commercial consequences and how it leads to possible negative consequences.
Robust subspace clustering via multi-affinity matrices fusion

Subspace clustering (SC) methods have attracted widespread attention from interested researchers, and a variety of SC methods have been proposed recently. In general, SC methods obtain an affinity matrix, on which spectral clustering is used. Obviously, affinity matrices obtained by different algorithms are not the same, and their clustering effects are also different. A question then arises of whether the affinity matrices obtained by different algorithms can be merged to improve the clustering performance. The answer may be yes I n this paper, we design a new SC method, i.e., robust subspace clustering via multi-affinity matrices fusion (RSC/MAMF). Specifically, several classical SC algorithms, i.e., sparse subspace clustering (SSC), low-rank representation (LRR) and least squares regression (LSR), are first chosen to derive their own affinity matrices. To make full use of the information of different affinity matrices, and to mine the subspace structure thoroughly, we further fuse the derived affinity matrices, i.e., splice them into a 3-order tensor, and impose a weighted tensor nuclear norm (WTNN) to it, which not only mines and fuses the information of the different affinity matrices but also removes their noise. Additionally, to further explore the consistency of the different affinity matrices, spectral embedding is also unified into the final objective function. We propose an optimization algorithm to address the optimization problem of RSC/MAMF, which utilizes the Augmented Lagrange Multiplier (ALM) method. Experiments show that the multi-affinity matrices fusion idea is feasible, and RSC/MAMF outperforms the state-of-the-art SC methods.
Effective full connection neural network updating using a quantized full FORCE algorithm

This paper presents a new training algorithm that can update the situation of layers' network, and therefore, connections, neurons, and firing rate of neurons based on FORCE (first-order reduced and controlled error) training algorithm. The Quantized Full FORCE algorithm (QFF) also updates the number of neurons and connections between different layers in the network per iteration in a way that the whole firing rate of each layer is updated via selecting the best neurons and combining strong features. The update method is sequential, so that with each instance passing through the network, the network structure is updated with the Full FORCE algorithm. The algorithm updates the structure of networks with a multiple/single middle layer of the supervised version of feed forward networks such as Multilayer perceptron (MLP), changing them into partially-connected networks. A combination of principal component analysis PCA and Linear Discriminant Analysis (LDA) algorithms has been used to cluster the network input features. The paper focuses on the deep supervised MLP network with backpropagation (BP) and various datasets and its comparison with other MLP based stat of art methods and hybrid evolutionary algorithms. We achieved 98.15 percent accuracy for facial expression 98.6 and 97.7 percent for Wisconsin breast Cancer and Iris Flower in respectively. The training algorithm employed in the study enjoys a lower computational complexity while yielding faster and more accurate convergence, starting with a very low level of errors of 0.009 in comparison with the full connection network and it solves the challenge of getting stuck in local minima and poor convergence of Gradient Decent with BP.
Fluid inclusions, H-O-S-Pb isotopes, and Rb-Sr geochronology of the Augaro gold deposit, West Eritrea, NE Africa: Implications for ore genesis

Northeast Africa is an emerging world-class gold province due to the numerous lode gold deposits with Au resources of over 43 Moz discovered in the Nubian Shield. The Augaro gold deposit, located in the Adobha-Augaro Cu-Au belt of west Eritrea, is composed of several auriferous quartz veins. The orientations of the auriferous quartz veins in this deposit are strictly controlled by NE-trending shear zones and nearly E-trending fractures. The most common metallic mineral is pyrite, and gold has been identified as native gold, electrum, and calaverite. Fluid inclusion study, H-O-S-Pb isotope analysis, and Rb-Sr dating were carried out in the Augaro gold deposit. A petrographic investigation revealed that fluid inclusions of type W (Liquid-rich aqueous), type C (CO2-rich), and type S (halite-bearing) are prevalent in auriferous quartz veins. The H2O, NaCl, and CO2 are the main components of the metallogenic fluids, which are medium-low temperatures (150–360 °C) and medium to low salinities (<34 wt% NaCl eqv.). The physicochemical features of fluid inclusions indicate that magmatic fluids experienced phase separation earlier in their evolution and mixed with meteoric water subsequently. Rb-Sr dating of fluid inclusions in auriferous quartz suggests that the Augaro gold deposit formed during the post-orogenic stage (543 ± 14 Ma) of East African orogeny in the Late Neoproterozoic, which is a new metallogenic epoch for the lode gold deposits in Nubian Shield. Geological and Geochemical features refer to the Augaro gold deposit being an intrusion-related gold deposit (IRGD).
A collective way of teaching mathematics as a means of forming self-control among students of grades 5-6

The article presents an overview of methodological approaches to the formation of self-control among students of grades 5-6 in the study of mathematics. The analysis of the state of this issue showed that in the works of D.B. Elkonin, G.A. Zukerman, V.L. Krutetsky, etc., the importance of self-control within the framework of organizing collective work in the classroom is indicated, and in the works of A.G. Mordkovich, Yu.M. Kolyagin, etc. when studying mathematics. It is proved that one of the means of forming self-control among students of grades 5-6 is a collective way of learning.