Reviewerly/exHarmony huggingface dataset:
Mapping soil organic carbon fractions for Australia, their stocks, and uncertainty

Abstract. Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric-log-ratio (ilr) transformation to account for the closed compositional nature of SOC fractions. The resulting back-transformed ilr components were mapped across Australia. SOC fraction stocks for 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments, and soil thickness. Mapping was done by a quantile regression forest fitted with the ilr-transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17 %, whereas 28 % ± 17 % was PyOC and 13 % ± 11 % was POC. The allocation of total organic carbon (TOC) to the MAOC fractions increased with depth. SOC vulnerability (i.e. POC/[MAOC+PyOC]) was greater in areas with Mediterranean and temperate climates. TOC and the distribution among fractions were the most influential variables in SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates' importance metric. We estimated the total SOC stocks (0–30 cm) to be 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as a response to land use change, management, and climate change.
One AI Does Not Fit All: A Cluster Analysis of the Laypeople’s Perception of AI Roles

Artificial intelligence (AI) applications have become an integral part of our society. However, studying AI as one entity or studying idiosyncratic applications separately both have limitations. Thus, this study used computational methods to categorize ten different AI roles prevalent in our everyday life and compared laypeople's perceptions of them using online survey data (N = 727). Based on theoretical factors related to the fundamental nature of AI, the principal component analysis revealed two dimensions that categorize AI: human involvement and AI autonomy. K-means clustering identified four AI role clusters: tools (low in both dimensions), servants (high human involvement and low AI autonomy), assistants (low human involvement and high AI autonomy), and mediators (high in both dimensions). Multivariate analyses of covariances revealed that people assessed AI mediators the most and AI tools the least favorably. Demographics also influenced laypeople's assessments of AI. The implications of these results are discussed.
Permutation‐based true discovery proportions for functional magnetic resonance imaging cluster analysis

Summary We propose a permutation‐based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well‐known spatial specificity paradox. We offer a user‐friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family‐wise error rate for multiple testing and taking into account that the cluster was chosen in a data‐driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.
A Lightweight Deep Learning Model for Automatic Modulation Classification Using Residual Learning and Squeeze–Excitation Blocks

Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise. However, their efficacy depends on the complexity of the algorithm, which can be characterized by the number of parameters. In this research, we presented a deep learning algorithm for AMC, inspired by residual learning, which has remarkable accuracy and great representational ability. We also employed a squeeze-and-excitation network that is capable of exploiting modeling interconnections between channels and adaptively re-calibrates the channel-wise feature response to improve performance. The proposed network was designed to meet the accuracy requirements with a reduced number of parameters for efficiency. The proposed model was evaluated on two benchmark datasets and compared with existing methods. The results show that the proposed model outperforms existing methods in terms of accuracy and has up to 72.5% fewer parameters than convolutional neural network designs.
Time-shift selection for reservoir computing using a rank-revealing QR algorithm

Reservoir computing, a recurrent neural network paradigm in which only the output layer is trained, has demonstrated remarkable performance on tasks such as prediction and control of nonlinear systems. Recently, it was demonstrated that adding time-shifts to the signals generated by a reservoir can provide large improvements in performance accuracy. In this work, we present a technique to choose the time-shifts by maximizing the rank of the reservoir matrix using a rank-revealing QR algorithm. This technique, which is not task dependent, does not require a model of the system and, therefore, is directly applicable to analog hardware reservoir computers. We demonstrate our time-shift selection technique on two types of reservoir computer: an optoelectronic reservoir computer and the traditional recurrent network with a t a n h activation function. We find that our technique provides improved accuracy over random time-shift selection in essentially all cases.
How to Model Privacy Threats in the Automotive Domain

This paper questions how to approach threat modelling in the automotive domain at both an abstract level that features no domain-specific entities such as the CAN bus and, separately, at a detailed level. It addresses such questions by contributing a systematic method that is currently affected by the analyst's subjectivity because most of its inner operations are only defined informally. However, this potential limitation is overcome when candidate threats are identified and left to everyone's scrutiny. The systematic method is demonstrated on the established LINDDUN threat modelling methodology with respect to 4 pivotal works on privacy threat modelling in automotive. As a result, 8 threats that the authors deem not representable in LINDDUN are identified and suggested as possible candidate extensions to LINDDUN. Also, 56 threats are identified providing a detailed, automotive-specific model of threats.
Quality control system for mammographic breast positioning using deep learning

This study proposes a deep convolutional neural network (DCNN) classification for the quality control and validation of breast positioning criteria in mammography. A total of 1631 mediolateral oblique mammographic views were collected from an open database. We designed two main steps for mammographic verification: automated detection of the positioning part and classification of three scales that determine the positioning quality using DCNNs. After acquiring labeled mammograms with three scales visually evaluated based on guidelines, the first step was automatically detecting the region of interest of the subject part by image processing. The next step was classifying mammographic positioning accuracy into three scales using four representative DCNNs. The experimental results showed that the DCNN model achieved the best positioning classification accuracy of 0.7836 using VGG16 in the inframammary fold and a classification accuracy of 0.7278 using Xception in the nipple profile. Furthermore, using the softmax function, the breast positioning criteria could be evaluated quantitatively by presenting the predicted value, which is the probability of determining positioning accuracy. The proposed method can be quantitatively evaluated without the need for an individual qualitative evaluation and has the potential to improve the quality control and validation of breast positioning criteria in mammography.
Digital competence of the modern teacher of the new Ukrainian school: 2023 (the search for solutions during the war)

The collection contains materials from the reports of the annual all-Ukrainian scientific and practical seminar "Digital competence of the modern teacher of the new Ukrainian school: 2023 (the search for solutions during the war). The issues of overcoming the challenges in the educational process caused by the large-scale aggression of the Russian Federation in Ukraine are outlined. The experience and best practices of creating a safe digital educational environment in institutions of general secondary education are presented. Practical steps in the direction of digitalization of education and development of digital competence of the teacher are highlighted. Theoretical approaches and practical developments regarding the use of ICT in professional education and teacher training are described. For developers of educational policy, teachers, scientists, managers, teachers, doctoral students, graduate students, students, the general pedagogical public. Materials are published in the author's editorial office. The authors are responsible for the accuracy of the information provided.
A survey of blockchain: concepts, applications and challenges

With the development of Bitcoin, organisations, be it businesses or institutions, are centring on leveraging Bitcoin's blockchain technology to nonmonetary based applications to improve efficiency of the activities. Having various benefits like anonymity, decentralised, audibility etc. Blockchain technology can be vastly implemented in various sectors other than financial too. This paper gives an overview the blockchain technology. It briefs about various technical concepts used in the blockchain, its types and where it can be used. It also discusses some proposed applications of the technology and tools or frameworks that can be used to develop such. It also presents the limitations of the technology.
A flexible calibration method for large-range binocular vision system based on state transformation

Vision measurement is becoming an indispensable technology in construction scenarios. The calibration result of the binocular camera directly affects the accuracy of vision measurement. Aiming at the problem that it is challenging to calibrate the large-range binocular vision system conveniently in harsh construction environments, a flexible binocular camera calibration method based on state transformation is proposed. First, the mapping between the pixel coordinates in the image of the incomplete target and the corresponding world coordinates is realized based on the global locator. Second, the calibration process is divided into two stages. The lenses are focused on the calibration position, and the non-overlapping fields of view calibration in the initial state is achieved using virtual projection point pairs. Then the lenses are focused on the measurement position. Some extrinsic parameters of the monocular camera are solved independently by using the properties of the rotation matrix, and the remaining parameters are calculated by the depth constraint. On this basis, the state transformation matrices are solved, and the relative extrinsic parameters of the binocular camera are obtained. Finally, a series of simulations and experiments are carried out. The results demonstrate that the mean reprojection error of the monocular camera is only 0.0675 pixels. The relative measurement error of binocular vision is less than 1.75%. In addition, the proposed method shows good robustness under different target angles and calibration distances. The method can be applied to complex engineering sites.
Feature identity determines representation structure in working memory.

Visual working memory maintains both continuous-perceptual information and discrete-categorical information about memory items. Both types of information are represented in working memory, but the representation structure remains unknown. Continuous and categorical information about a single stimulus could be represented separately, in two different representations. Alternatively, continuous and categorical information could be represented jointly as a single representation. To investigate this, we fitted two different computational models to delayed estimation data assuming either separate or joint representations of continuous and categorical information in working memory, for three different, commonly used features (orientation, color, and shape). Across a set of nine experiments, model fits clearly show that feature identity drives the representation structure, with a joint-representation structure for orientation, but a separate-representations structure for color and shape. This pattern was remarkably invariant across a variety of task contexts. Existing models miss this distinction, leading to mischaracterization of memory precision. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
How Do Paternalistic Leaders Motivate Employees’ Information Security Compliance? Building a Climate and Applying Sanctions

This paper examines the underlying mechanisms through which paternalistic leadership (PL) motivates employees’ information systems policy (ISP) compliance. We propose that the three dimensions of PL—authoritarian leadership (AL), benevolent leadership (BL), and moral leadership (ML)—influence employees’ ISP compliance by affecting their perceptions of two information security control mechanisms: sanctions and the information security climate. Based on survey data from 760 participants, we found that the impact of AL is partially mediated by employees’ perceptions of sanctions, the impact of BL is partially mediated by employees’ perceptions of the information security climate, and the impact of ML is partially mediated by employees’ perceptions of both sanctions and the information security climate. Our research extends the existing literature by exploring the impact of specific leadership styles on employees’ perceptions of information security control mechanisms and by proposing that perceptions of information security control mechanisms play a mediating role between PL and ISP compliance. The findings suggest that in addition to choosing effective control mechanisms, it is also important for leaders to adjust their leadership style to ensure that employees perceive control mechanisms in the expected manner.
Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data

Development of deep convolutional neural networks for breast cancer classification has taken significant steps towards clinical adoption. It is though unclear how the models perform for unseen data, and what is required to adapt them to different demographic populations. In this retrospective study, we adopt an openly available pre-trained mammography breast cancer multi-view classification model and evaluate it by utilizing an independent Finnish dataset. Transfer learning was used, and the pre-trained model was finetuned with 8,829 examinations from the Finnish dataset (4,321 normal, 362 malignant and 4,146 benign examinations). Holdout dataset with 2,208 examinations from the Finnish dataset (1,082 normal, 70 malignant and 1,056 benign examinations) was used in the evaluation. The performance was also evaluated on a manually annotated malignant suspect subset. Receiver Operating Characteristic (ROC) and Precision–Recall curves were used to performance measures. The Area Under ROC [95%CI] values for malignancy classification obtained with the finetuned model for the entire holdout set were 0.82 [0.76, 0.87], 0.84 [0.77, 0.89], 0.85 [0.79, 0.90], and 0.83 [0.76, 0.89] for R-MLO, L-MLO, R-CC and L-CC views respectively. Performance on the malignant suspect subset was slightly better. On the auxiliary benign classification task performance remained low. The results indicate that the model performs well also in an out-of-distribution setting. Finetuning allowed the model to adapt to some of the underlying local demographics. Future research should concentrate to identify breast cancer subgroups adversely affecting performance, as it is a requirement for increasing the model’s readiness level for a clinical setting.
Random Graph Matching at Otter’s Threshold via Counting Chandeliers

We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex. For two Erdős–Rényi graphs G(n,q) whose edges are correlated through a latent vertex correspondence, we show that this algorithm correctly matches all but a vanishing fraction of the vertices with high probability, provided that nq→∞ and the edge correlation coefficient ρ satisfies ρ2>α ≈ 0.338, where α is Otter's tree-counting constant. Moreover, this almost exact matching can be made exact under an extra condition that is information-theoretically necessary. This is the first polynomial-time graph matching algorithm that succeeds at an explicit constant correlation and applies to both sparse and dense graphs. In comparison, previous methods either require ρ=1−o(1) or are restricted to sparse graphs.
Safety Helmet-Wearing Detection System for Manufacturing Workshop Based on Improved YOLOv7

Safety helmets play a vital role in protecting workers’ heads. In order to improve the accuracy of the detection model in complex environments, such as complex backgrounds and different lighting and distances, we propose a safety helmet-wearing detection algorithm based on the improved YOLOv7. In the backbone network, 16-channel features are used to replace 3-channel RGB features. Structured pruning is performed in the head network, and the loss function is replaced by SIoU. Experiments on the “helmet-head,” “helmet-data,” and “helmet” data sets show that the mAP and F1 of YOLOv7_ours improved in this paper are better than Faster RCNN, YOLOv5, and YOLOv7 series models. On image data of different application scenarios, light intensity, and color depth, YOLOv7_ours has better stability and higher accuracy and can detect at 112.4FPS (1000/8.9). Based on the improved YOLOv7_ours, we integrated face recognition technology and text-to-speech (TTS) to realize helmet detection, identity recognition, and automatic voice reminder capabilities and developed a safety helmet-wearing detection prototype system. We verified the feasibility of the helmet detection algorithm and system in the semifinished product manufacturing workshop.
Secure Convolution Neural Network Inference Based on Homomorphic Encryption

Today, the rapid development of deep learning has spread across all walks of life, and it can be seen in various fields such as image classification, automatic driving, and medical imaging diagnosis. Convolution Neural Networks (CNNs) are also widely used by the public as tools for deep learning. In real life, if local customers implement large-scale model inference first, they need to upload local data to the cloud, which will cause problems such as data leakage and privacy disclosure. To solve this problem, we propose a framework using homomorphic encryption technology. Our framework has made improvements to the batch operation and reduced the complexity of layer connection. In addition, we provide a new perspective to deal with the impact of the noise caused by the homomorphic encryption scheme on the accuracy during the inference. In our scheme, users preprocess the images locally and then send them to the cloud for encrypted inference without worrying about privacy leakage during the inference process. Experiments show that our proposed scheme is safe and efficient, which provides a safe solution for users who cannot process data locally.
Integrated solar PV and piezoelectric based torched fly ash tiles for smart building applications with machine learning forecasting

Solar photovoltaic (PV) power generation projects are rapidly increasing since the necessity for electricity has increased in developing countries like India. Still, the complexity of technical requirements narrows the financial margins of the construction industry. Limited researchers have investigated PV manufacturing, planning and techniques for electricity generation. Additionally a new method of generating power via the use of a human population has been developed. A piezoelectric transducer is used to detect the vibration. When a footstep applies pressure to the piezoelectric transducer, the pressure or force is converted to electrical energy. This experimental research investigates the photovoltaic sensors and piezoelectric transducers incorporated floor tiles in a residential building for power generation. The performance of the sensors were analysed experimentally and analytically by using machine learning models such as ARIMA time series model, Support Vector Machine (SVM) and k-nearest neighbour (KNN). The prediction models were validated through assessment matrix such as Root Mean Square Error (RMSE). From the experiments, it was observed that voltage is high during the day when a load is applied, and a low amount is acquired at night when no load is applied. And from the analytical study, the ARIMA time series model has the highest prediction accuracy. Error analysis was done to demonstrate the efficiency of the proposed models. The accuracy of the developed models can be further increased, which is subject to future research.
Exploratory Research Design in Management Science: A Review of Literature on Conduct and Application

The study examined the conduct and application of exploratory research design in management science. The study adopted an exploratory research design. Articles that utilised exploratory research design in management sciences were reviewed using a content analysis. Further, an evaluation of the types and methodologies, conduct, and application of exploratory research design in the field of management sciences was carried out. The study’s findings revealed that some researchers used exploratory research design appropriately in their studies, whereas others did not, resulting in inappropriate research design usage. The study concluded that knowledge of exploratory research design could help researchers better define, understand research problems, and advance research in management sciences. The study recommended that, researchers should choose research designs that they are most comfortable with and feel most competent to handle, but the choice should ideally be based on the nature of the research phenomenon being studied and information available.