Reviewerly/exHarmony huggingface dataset:
Carbon dioxide emission reduction potential for low income housing units based on photovoltaic systems in distinct climatic regions

Growing concern on climate change has supported technologies that reduce carbon dioxide emissions. The emission factor of the Brazilian grid electricity is low due to hydropower dominance. However, new hydropower plants have migrated to the Amazon region, with likely social and environmental consequences and rainfall shortage has demanded costly fossil fueled power plants. The grid's emission factor is increasing and new renewable energy alternatives such photovoltaic (PV) are one option to diversify the electricity mix. However, PV systems' efficiency in Brazil varies depending on the climatic region of its installation. This work evaluated the variance in CO2 emissions reduction of grid connected PV systems for low-income housing in different Brazilian climatic regions. The analysis was based on the PV system, installed in low-income housing projects. Electricity generation was determined through RETScreen and CO2 emission reductions were determined considering the emission factor of the national grid electricity. Variation of CO2 emission reductions among bioclimatic regions reached up to 12%. Therefore, PV systems should be adopted in all low-income housing projects in Brazil, regardless of their region, to reduce household's expenditures, CO2 emissions, and democratize the technology.
A Novel Secure End-to-End IoT Communication Scheme Using Lightweight Cryptography Based on Block Cipher

Personal data security is a cybersecurity trend that has captured the world’s attention. Governments, practitioners and academics are jointly building personal data security in various communication systems, including IoT. The protocol that is widely used in IoT implementation is MQTT. By default, MQTT does not provide data security features in the form of data encryption. Therefore, this research was carried out on the design of Secure End-to-End Encryption MQTT with Block Cipher-Based Lightweight Cryptography. The protocol is designed by utilizing the Galantucci secret sharing scheme and a lightweight cryptographic algorithm based on a block cipher. The algorithms used include AES-128 GCM mode, GIFT-COFB, Romulus N1, and Tiny JAMBU. We tested the Secure End-to-End for MQTT protocol on the ARM M4 and ESP8266 processors. Our testing results on NodeMCU board, Tiny JAMBU have an average encryption time of 313 μs and an average decryption time of 327 μs. AES-128 GCM mode has an average encryption time of 572 μs and an average decryption time of 584 μs. GIFT-COFB has an average encryption time of 1094 μs and an average decryption time of 1110 μs. Meanwhile, Romulus N1 has an average encryption time of 2157 μs and an average decryption time of 2180 μs. On STM32L4 discovery, Tiny JAMBU had average encryption of 82 μs and an average decryption time of 85 μs. AES-128 GCM mode has an average encryption time of 163 μs and an average decryption time of 164 μs. GIFT-COFB has an average encryption time of 164 μs and an average decryption time of 165 μs. Meanwhile, Romulus N1 has an average encryption time of 605 μs and an average decryption time of 607 μs. Our experiment shows that the fastest performance is produced by Tiny JAMBU, followed by AES-128 Mode GCM, GIFT-COFB and Romulus N1.
S2-PCM: Super-Resolution Structural Point Cloud Matching for High-Accuracy Video-SAR Image Registration

In this paper, the super-resolution structural point cloud matching (S2-PCM) framework is proposed for video synthetic aperture radar (SAR) inter-frame registration, which consists of a feature recurrence super-resolution network (FRSR-Net), structural point cloud extraction network (SPCE-Net) and robust point matching network (RPM-Net). FRSR-Net is implemented by integrating the feature recurrence structure and residual dense block (RDB) for super-resolution enhancement, SPCE-Net is implemented by training a U-Net with data augmentation, and RPM-Net is applied for robust point cloud matching. Experimental results show that compared with the classical SIFT-like algorithms, S2-PCM achieves higher registration accuracy for video-SAR images under diverse evaluation metrics, such as mutual information (MI), normalized mutual information (NMI), entropy correlation coefficient (ECC), structural similarity (SSIM), etc. The proposed FRSR-Net can significantly improve the quality of video-SAR images and point cloud extraction accuracy. Combining FRSR-Net with S2-PCM, we can obtain higher inter-frame registration accuracy, which is crucial for moving target detection and shadow tracking.
Divine Becoming in the Postdigital

Most engagements between theology, religious practices and the digital have focussed upon the practicalities of these processes. Little attention has yet been paid to the possible impact of the postdigital upon actual beliefs, particularly about the nature of God. If it is the case that the postdigital defines the context in which we are now operating, then it seems that such questions need to be addressed. A number of different approaches to this issue will be examined, ranging from those which either see God and the digital as external to each other such as that of Pope Francis, to those which see no distinction between the two and where all aspects of human life are contained and encompassed within God’s activity in and through Creation. It will be argued that neither of these enables the critical approach to the postdigital that is required, not just because of the impacts of the digital upon belief itself, but also because of the power of the Big Tech companies which are now so dominant. A more nuanced interpretation of how God and Creation relate and interact will be presented, drawing on concepts from New Materialism and Relational Christian Realism, and the ideas of presence and absence, proximity and distance, which are to be mined from those sources.
Shifting Design Perspectives: Touch, Co-Location, and Sharing Objects during the Pandemic

HCI research has explored a variety of technologies to support human-to-human touch by simulating or mediating the feeling of touch over distance. Restrictions in activities involving touch, close contact, and physical proximity due to the COVID-19 pandemic have made it more pertinent to understand and investigate the goals, opportunities, and limitations of technologically mediated touch. We conducted an exploratory interview study with 18 participants across the United States in April and June of 2021 to understand how the COVID-19 pandemic has changed people’s attitudes towards engaging in touch, being co-located, and sharing physical objects. We observed four themes: new efforts to evaluate safety, new preferences for physically co-locating without touch, technology solutions amplifying separation, and a newfound appreciation for human touch and touch-adjacent activities. These results suggest that future touch technologies may need to shift focus from simulating human touch convincingly to allowing for interactions that incorporate these emergent sensitivities to safety and preferences for physical co-location.
Process Theology Against Global Capitalism: Towards an Ecological Civilization

This conversation between Catherine Keller and Petar Jandrić explores the contemporary relevance of the concept of Apocalypse and inquires into ways of responsible reading of Biblical texts. It introduces the concept of dreamreading and positions it in relation to the concept of prophecy. It presents arguments for rejecting the notion of creatio ex nihilo and proposes a close examination of the theology concerning creatio ex profundis. The conversation outlines the basics of process theology and its complex links to postmodernism and feminism. It moves to the contradiction between universal morality expressed in Scripture and postmodernism’s rejection of universal morality. It outlines Keller’s understanding of theopoetics and explores the transdisciplinary nature of (process) theology. The conversation ends with an in-depth exploration of postdigital theology, addressing postdigital spatio-temporality, transhumanism, posthumanism, Trinity, eco-pedagogy, and the Kingdom of God.
SETR-YOLOv5n: A Lightweight Low-Light Lane Curvature Detection Method Based on Fractional-Order Fusion Model

End-to-end automatic driving requires the identification of lane curvature.We proposed a lightweight detection method for the low-light lane curvature based on the Fractional-Order Fusion Model (FFM) to assure real-time performance and increase the reliability of automatic driving in lowlight conditions.To begin, the FFM method is introduced to enhance images with low average brightness, fuzzy detail, and a high signal-to-noise ratio.Under low-light conditions, these images cannot clearly express information (such as rainy, snowy, foggy, and other harsh external environments).Then, aiming at the problems of complex network structure, high hardware configuration required for training, and low transmission Frames Per Second (FPS) of real-time detection in the previously proposed YOLOv5, the SETR-C3Block module is proposed.The YOLOv5n is improved by optimizing the configuration of the target detector head and the network's structure, which solves the problems of low efficiency and redundancy parameters in feature extraction in the network.According to the experimental results on the lane curvature dataset, the mAP@.5:.95 of SETR-YOLOv5n is 87.22%, the transmission FPS of real-time detection is 70.4,and the number of model parameters is only 1.8M.It shows that the SETR-YOLOv5n can meet the lightweight and accuracy requirements of target detection by the mobile terminal or embedded device.
DualG-GAN, a Dual-channel Generator based Generative Adversarial Network for text-to-face synthesis

Text-to-image synthesis is a fundamental and challenging task in computer vision, which aims to synthesize realistic images from given descriptions. Recently, text-to-image synthesis methods have achieved great improvements in the quality of synthesized images. However, very few works have explored its application in the scenario of face synthesis, which is of great potentials in face-related applications and the public safety domain. On the other side, the faces generated by existing methods are generally of poor quality and have low consistency to the given text. To tackle this issue, in this paper, we build a novel end-to-end dual-channel generator based generative adversarial network, named DualG-GAN, to improve the quality of the generated images and the consistency to the text description. In DualG-GAN, to improve the consistency between the synthesized image and the input description, a dual-channel generator block is introduced, and a novel loss is designed to improve the similarity between the generated image and the ground-truth in three different semantic levels. Extensive experiments demonstrate that DualG-GAN achieves state-of-the-art results on SCU-Text2face dataset. To further verify the performance of DualG-GAN, we compare it with the current optimal methods on text-to-image synthesis tasks, where quantitative and qualitative results show that the proposed DualG-GAN achieves optimal performance in both Fréchet inception distance (FID) and R-precision metrics. As only a few works are focusing on text-to-face synthesis, this work can be seen as a baseline for future research.
Adaptive toolkit of branch-oriented workshop environment for enlargement the cloud-based e-learning media platform

The ways of providing comprehensive efficiency increase in communication facilities of the academic space are given with regard to stipulated methods of managing distributed network resources. Selected the user interfaces types are distinguished according to user actions in the studied subject area, which made it possible to justify and hierarchically organize the categories of adaptive toolkit of the branch-oriented workshop environment by the classes of components declared in the project, which are closely related to the scheme of learning experiment and are basic means for simulating transients. The analytical models of classes of components of the virtual laboratory stand are compiled, the elements of which represent the properties and methods for visualization and further processing of interacting instances of the basic locations of the subject area, while ensuring system stability and controllability by clear distribution of functionality. Finally, the unification of component set template properties of the subject area is implemented, which greatly extending the targeted destination of virtual platform and increasing number of educational disciplines of academic course covered by the designed media resource. The results of the pedagogical verification showed an increase in the students’ performance in mastering the subject area by means of presented branch-oriented workshop environment.
IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation

Tiny spike-like projections on the basement membrane of glomeruli are the main pathological feature of membranous nephropathy at stage II (MN II), which is the most significant stage for the diagnosis and treatment of renal disease. Pathological technology is the gold standard in the diagnosis of spike-like and other MNs, and automatic classification of spike-like projection is a crucial step in assisting pathologists in their diagnosis. However, owing to hard-to-label spile-like projections and the scarcity of patient data, classification of glomeruli with spike-like projections based on supervised learning methods is a challenging task.To overcome the aforementioned problems, the idea of integrating weakly-supervised learning and data augmentation methods is utilized in designing the classification framework. Specifically, a multiple instance learning with instance-level data augmentation (IDA-MIL) method for the classification of glomeruli with spike-like projections is established in this paper. The proposed classification framework first trains the MIL model on a dataset with image-level labels, and the well-trained MIL model is used to extract instances that include spike-like projections in the whole glomerular image. Then, rather than using an image-level generative adversarial network (ImgGAN), an instance-level generative adversarial network (InsGAN) based on the StyleGAN2-ADA model is trained on the spike-like instances obtained by the MIL model and synthesizes new spike-like projection instances. Finally, the synthesized spike-like instances are extended to the training dataset to further improve the classification performance of MIL.The designed IDA-MIL model is verified and evaluated from two aspects based on the in-house dataset. On the one hand, the performance comparisons between InsGAN and ImgGAN on five metrics, which involve FID, KID, Precision, Recall and IS, show that InsGAN obtains a better score and can synthesize effective spike-like projections. However, the proposed IDA-MIL model achieves the best classification performance with an accuracy of 0.9405. Then, to make nephrologists believe the inference result of the proposed model, we use heatmap technology to visualize the basis of the model inferences and show the top 4 probability spike-like instances per glomerulus. Furthermore, we analyze the correlation between the disease and the proportion of spike-like instances in bags from historical cases.Compared with the ImgGAN, the InsGAN can synthesize natural and varied spike-like projections, which results in the classification performance of the MIL model achieving great improvement by adding synthesized instance samples into the training dataset. The heatmap of spike-like inferences and the proportion of spike-like instances can help nephrologists to make a preliminary reliable diagnosis in clinical practice. This work provides a valuable reference for medical image classification with limited data and small-scale lesions based on deep learning.
Approaches to the choice of tools for adaptive learning based on highlighted selection criteria

The article substantiates the relevance of adaptive learning of students in the modern information society, reveals the essence of such concepts as “adaptability” and “adaptive learning system”. It is determined that a necessary condition for adaptive education is the criterion of an adaptive learning environment that provides opportunities for advanced education, development of key competencies, formation of a flexible personality that is able to respond to different changes, effectively solve different problems and achieve results. The authors focus on the technical aspect of adaptive learning. Different classifications of adaptability are analyzed. The approach to the choice of adaptive learning tools based on the characteristics of the product quality model stated by the standard ISO / IEC 25010 is described. The following criteria for the selecting adaptive learning tools are functional compliance, compatibility, practicality, and support. By means of expert assessment method there were identified and selected the most important tools of adaptive learning, namely: Acrobatiq, Fishtree, Knewton (now Wiliy), Lumen, Realize it, Smart Sparrow (now Pearson). Comparative tables for each of the selected tools of adaptive learning according to the indicators of certain criteria are given.
Dual-grained directional representation for infectious disease case prediction

The uncertain infection transmission causes challenges in accurate disease prediction. Numerous methods have been proposed to capture the temporal pictures from past observations within equal time intervals, which are called single-grained time series. However, these methods are not suitable for capturing uncertain temporal dynamics from infectious disease time series, since the infectious diseases may propagate in the incubation period. To address this issue, this paper proposes a Dual-Grained Directional Representation (DGDR) to generate predictions, via consolidating the representations of an equal-grained time series and several fine-grained time series. Firstly, the proposed DGDR learns a transformed segmentation into three kinds of representations. And then those representations from both equal-grained data and fine-grained data are temporally consolidated to connect with outputs. Extensive experiments on two real infectious disease datasets are done to validate the proposed DGDR. Compared with the other twelve methods, MAE value is decreased by 31.5%, RMSE value is decreased by 29.9%, and R 2 value is improved by 87.6%.
Connectivity-aware Graph: A planar topology for 3D building surface reconstruction

Multi-view Stereo (MVS) meshes suffer from occlusions or missing data, making most surface reconstruction methods invalid. Due to data imperfections, existing methods, just like PolyFit, have made a trade-off between reconstruction accuracy and time consumption. We propose a novel approach that automatically reconstructs a building surface model from raw triangular mesh with data incompleteness. Unlike existing methods that extract high-quality primitives, our method focuses on assemblies of primitives to control the level of geometric detail in the reconstructed models. We design a topological relationship of the primitives to form a plane connection graph. To be precise, we first combine the scalability of shapes with plane primitives. Then strong and soft connections between primitives are constructed by calculating the confidence of the plane intersection in space. Furthermore, the topological relations of all primitives are encoded into an undirected graph. Finally, a watertight and manifold model is extracted from the faces of a candidate set by energy minimization. Experiments on the Helsinki 3D dataset demonstrate the superiority of our method in time consumption and reconstruction error, as measured by Hausdorff distance. Our method outperforms other primitive-based algorithms in handling non-planar structures. Even when dealing with imperfect data, a watertight model is still obtained.
Fintech meets Industry 4.0: a systematic literature review of recent developments and future trends

The Fintech and Industry 4.0 paradigms, and the combination of the two, have gained increasing attention from the scientific community in recent years. This paper aims to explore the convergence of these two research themes by framing their interplay through a systematic literature review covering the period of 2017 to August 2021 and involving 78 papers. The bibliometric approach made it possible to extract a large amount of useful information which was previously scattered and to present it in a systematic way. The analysis of the co-occurrence network of keywords identified four distinct clusters, corresponding to the dominant research themes in the area, which suggested the design of a research agenda. The agenda-driven questions indicated which current aspects of Fintech and Industry 4.0 need additional insight. In that regard, the environmental and social responsibility aspects of the firms in the sectors were found of particular importance. Theoretical and practical implications of this study as well as answers provided to a set of initially posed research questions were also discussed.
LANet: Stereo matching network based on linear-attention mechanism for depth estimation optimization in 3D reconstruction of inter-forest scene

The 3D reconstruction of forests provides a strong basis for scientific regulation of tree growth and fine survey of forest resources. Depth estimation is the key to the 3D reconstruction of inter-forest scene, which directly determines the effect of digital stereo reproduction. In order to solve the problem that the existing stereo matching methods lack the ability to use environmental information to find the consistency of ill-posed regions, resulting in poor matching effect in regions with weak texture, occlusion and other inconspicuous features, LANet, a stereo matching network based on Linear-Attention mechanism is proposed, which improves the stereo matching accuracy by effectively utilizing the global and local information of the environment, thereby optimizing the depth estimation effect. An AM attention module including a spatial attention module (SAM) and a channel attention module (CAM) is designed to model the semantic relevance of inter-forest scenes from the spatial and channel dimensions. The linear-attention mechanism proposed in SAM reduces the overall complexity of Self-Attention from O(n2) to O(n), and selectively aggregates the features of each position by weighted summation of all positions, so as to learn rich contextual relations to capture long-range dependencies. The Self-Attention mechanism used in CAM selectively emphasizes interdependent channel maps by learning the associated features between different channels. A 3D CNN module is optimized to adjust the matching cost volume by combining multiple stacked hourglass networks with intermediate supervision, which further improves the speed of the model while reducing the cost of inferential calculation. The proposed LANet is tested on the SceneFlow dataset with EPE of 0.82 and three-pixel-error of 2.31%, and tested on the Forest dataset with EPE of 0.68 and D1-all of 2.15% both of which outperform some state-of-the-art methods, and the comprehensive performance is very competitive. LANet can obtain high-precision disparity values of the inter-forest scene, which can be converted to obtain depth information, thus providing key data for high-quality 3D reconstruction of the forest.