Reviewerly/exHarmony huggingface dataset:
A feasibility assessment of utilizing concentrated solar power (CSP) in the Zimbabwean regions of Hwange and Lupane

The current study assesses the feasibility of Zimbabwe's Hwange and Lupane regions to host a large-scale Concentrated Solar Power (CSP) facility. The study's overarching goal is to aid in identifying, classifying, and validating suitable sites for hosting a CSP facility. In this paper, suitable sites are identified and classified by coupling the multi-criteria decision-making (MCDM) technique (specifically the analytical hierarchy process, abbreviated AHP) and geographic information system (GIS) software. Following the identification of suitable regions, the validation is carried out by technical and economic measures. As a specific criterion for decision-making, a geographic database was developed utilizing layers provided by various data sets on irradiance, orography, location, and water resources. The final maps using special tools in ArcGIS Pro revealed that the land available for concentrating solar power in Hwange and Lupane is 1792 km2 (5.6% of the study area) and 3771 km2 (11.9% of the study area), respectively. A Monte Carlo Simulation (MCS) of the theoretical power potential revealed that suitable sites in Hwange and Lupane could technically generate power ranging from 380.0 TWh/year to 477.5 TWh/year and 878.8 TWh/year to 1125.0 TWh/year, respectively. The CSP facility without a thermal energy storage (TES) facility has a $ cost per kWh of 0.1879, while the CSP-TES hybrid costs 0.1468. The LCOE for CSP without TES and CSP with TES is $ 0.0679 and $ 0.0268 higher than Zimbabwe's electricity cost, respectively. Overall, results suggest that Lupane is an excellent location for CSP facilities and supports policymakers in establishing renewable energy tariffs, resulting in economic and sustainable development.
The Importance of Compliance as a Tool to Combat Cybercrime

Law 13.709/2018 - General Data Protection Law - is in force in Brazil, aiming to protect fundamental rights of freedom and personality. In this view, this proposal is closely linked to the analysis of the implementation of compliance rules in the business technology sectors in order to avoid cybercrime, identifying in practice what must be done aiming to eliminate the leakage of clients' data. This article will inform about the importance of compliance in cybercrime, how companies should provide conditions so that criminals do not have access to customer data. Business owners need to keep their technological security level very high, including several precautions explained throughout this paper. Companies must help in the fight against cyberattacks aiming to comply with specific laws. The need for the creation of specific rules will be demonstrated, thus avoiding Internet hacking. One cannot deny the advance of technology, but one must be aware that today we depend on it and that illegal acts will increasingly become a reality. What is sought is to identify specific means of combating cybercrime through the correct use of compliance in the technology sector of companies.
Multi-view stereoscopic attention network for 3D tumor classification in automated breast ultrasound

As the new generation of breast cancer screening tools, automated breast ultrasound (ABUS) has some difficulties of the long interpretation time in clinical application. A computer-aided diagnosis algorithm can assist diagnosticians in locating and classifying benign and malignant tumors in ABUS images quickly. However, locating and classifying ABUS images is challenging because they have inapparent and small lesion regions and blurry discriminative regions. This paper proposes the multi-view stereoscopic attention network (MVSA-Net) to address the challenges. First, MVSA-Net crops the three-dimensional (3D) lesion regions through the 3D localization unit. Second, MVSA-Net classifies the cropped lesion regions through the classification unit. The localization unit can pay more attention to lesion regions by constructing the stereoscopic attention module. Besides, the localization unit constructs the 3D localization tensor through the split output design. The classification unit can extract the stereoscopic features through the stereoscopic view and the plane features through the plane view based on the Transformer network. It also fuses the two features through the level connection design. Besides, it can more focus on the borderline region of the tumor based on the region-prior module. The paper uses 172 ABUS images to train MVSA-Net, and 42 ABUS images to test MVSA-Net. On the test set, the accuracy and AUC of MVSA-Net are 95.24% and 99.76%, respectively. MVSA-Net can assist diagnosticians in decreasing the interpretation time of ABUS images, thus helping more people screen breast tumors earlier.
She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models

Implicit gender bias in software development is a well-documented issue, such as the association of technical roles with men. To address this bias, it is important to understand it in more detail. This study uses data mining techniques to investigate the extent to which 56 tasks related to software development, such as assigning GitHub issues and testing, are affected by implicit gender bias embedded in large language models. We systematically translated each task from English into a genderless language and back, and investigated the pronouns associated with each task. Based on translating each task 100 times in different permutations, we identify a significant disparity in the gendered pronoun associations with different tasks. Specifically, requirements elicitation was associated with the pronoun “he” in only 6% of cases, while testing was associated with “he” in 100% of cases. Additionally, tasks related to helping others had a 91% association with “he” while the same association for tasks related to asking coworkers was only 52%. These findings reveal a clear pattern of gender bias related to software development tasks and have important implications for addressing this issue both in the training of large language models and in broader society.
Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting

Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network's activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies' future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.
SUBJECT-MATTER AND INTENSIONAL OPERATORS III: STATE-SENSITIVE SUBJECT-MATTER AND TOPIC SUFFICIENCY

Abstract Logical frameworks that are sensitive to features of sentences’ subject-matter—like Berto’s topic-sensitive intentional modals (TSIMs)—demand a maximally faithful model of the topics of sentences. This is an especially difficult task in the case in which topics are assigned to intensional formulae. In two previous papers, a framework was developed whose model of intensional subject-matter could accommodate a wider range of intuitions about particular intensional conditionals. Although resolving a number of counterintuitive features, the work made an implicit assumption that the subject-matter of an intensional conditional is a function of the subject-matters of its subformulae. This assumption—which I will call a principle of topic sufficiency —runs counter to some natural intuitions concerning topic. In this paper, we will investigate topic sufficiency and offer a semantic account that is state-sensitive , providing an implementation through the introduction of topic-sensitive logics related to William Parry’s prototypical $\mathsf {PAI}$ .
Reducing Uncertainty and Increasing Confidence in Unsupervised Learning

This paper presents the development of a novel algorithm for unsupervised learning called RUN-ICON (Reduce UNcertainty and Increase CONfidence). The primary objective of the algorithm is to enhance the reliability and confidence of unsupervised clustering. RUN-ICON leverages the K-means++ method to identify the most frequently occurring dominant centres through multiple repetitions. It distinguishes itself from existing K-means variants by introducing novel metrics, such as the Clustering Dominance Index and Uncertainty, instead of relying solely on the Sum of Squared Errors, for identifying the most dominant clusters. The algorithm exhibits notable characteristics such as robustness, high-quality clustering, automation, and flexibility. Extensive testing on diverse data sets with varying characteristics demonstrates its capability to determine the optimal number of clusters under different scenarios. The algorithm will soon be deployed in real-world scenarios, where it will undergo rigorous testing against data sets based on measurements and simulations, further proving its effectiveness.
DSRA-DETR: An Improved DETR for Multiscale Traffic Sign Detection

Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges in sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on improving multiscale detection performance. Our approach integrates the dilated spatial pyramid pooling model (DSPP) and the multiscale feature residual aggregation module (FRAM) to aggregate features at various scales. These modules excel at reducing feature noise and minimizing loss of low-level features during feature map extraction. Additionally, they enhance the model’s capability to detect objects at different scales, thereby improving the accuracy and robustness of traffic sign detection. We evaluate the performance of our method on two widely used datasets, the GTSDB and CCTSDB, and achieve impressive average accuracies (APs) of 76.13% and 78.24%, respectively. Compared with other well-known algorithms, our method shows a significant improvement in detection accuracy, demonstrating its superiority and generality. Our proposed method shows great potential for improving the performance of traffic sign detection for autonomous driving systems and will help in the development of safe and efficient autonomous driving technologies.
Global path planning and waypoint following for heterogeneous unmanned surface vehicles assisting inland water monitoring

The idea of dispatching multiple unmanned surface vehicles (USVs) to undertake marine missions has ignited a burgeoning enthusiasm on a global scale. Embarking on a quest to facilitate inland water monitoring, this paper presents a systematical approach concerning global path planning and path following for heterogeneous USVs. Specifically, by capturing the heterogeneous nature, an extended multiple travelling salesman problem (EMTSP) model, which seamlessly bridges the gap between various disparate constraints and optimization objectives, is formulated for the first time. Then, a novel Greedy Partheno Genetic Algorithm (GPGA) is devised to consistently address the problem from two aspects: (1) Incorporating the greedy randomized initialization and local exploration strategy, GPGA merits strong global and local searching ability, providing high-quality solutions for EMTSP. (2) A novel mutation strategy which not only inherits all advantages of PGA but also maintains the best individual in the offspring is devised, contributing to the local escaping efficiently. Finally, to track the waypoint permutations generated by GPGA, control input is generated by the nonlinear model predictive controller (NMPC), ensuring the USV corresponds with the reference path and smoothen the motion under constrained dynamics. Simulations and comparisons in various scenarios demonstrated the effectiveness and superiority of the proposed scheme.
Knowledge Tensor-Aided Breast Ultrasound Image Assistant Inference Framework

Breast cancer is one of the most prevalent cancers in women nowadays, and medical intervention at an early stage of cancer can significantly improve the prognosis of patients. Breast ultrasound (BUS) is a widely used tool for the early screening of breast cancer in primary care hospitals but it relies heavily on the ability and experience of physicians. Accordingly, we propose a knowledge tensor-based Breast Imaging Reporting and Data System (BI-RADS)-score-assisted generalized inference model, which uses the BI-RADS score of senior physicians as the gold standard to construct a knowledge tensor model to infer the benignity and malignancy of breast tumors and axes the diagnostic results against those of junior physicians to provide an aid for breast ultrasound diagnosis. The experimental results showed that the diagnostic AUC of the knowledge tensor constructed using the BI-RADS characteristics labeled by senior radiologists achieved 0.983 (95% confidential interval (CI) = 0.975-0.992) for benign and malignant breast cancer, while the diagnostic performance of the knowledge tensor constructed using the BI-RADS characteristics labeled by junior radiologists was only 0.849 (95% CI = 0.823-0.876). With the knowledge tensor fusion, the AUC is improved to 0.887 (95% CI = 0.864-0.909). Therefore, our proposed knowledge tensor can effectively help reduce the misclassification of BI-RADS characteristics by senior radiologists and, thus, improve the diagnostic performance of breast-ultrasound-assisted diagnosis.
Examination of Higher Education Teachers’ Self-Perception of Digital Competence, Self-Efficacy, and Facilitating Conditions: An Empirical Study in the Context of China

In the digital age, it is necessary for higher education teachers to improve their digital competence to face the challenges of the advancement of technology. Because there are few studies on the digital competence of higher education teachers in the context of Asia, the aim of this study was to describe Chinese higher education teachers’ self-perceptions of digital competence and investigate the effects of self-efficacy and facilitating conditions and how they predict teachers’ digital competence. For this purpose, a quantitative methodology was used. A final sample of 525 in-service higher education teachers from China completed the questionnaire. The data were analyzed using descriptive analysis, inferential analysis, and latent variable path analysis. The results indicate, first, that teachers’ general perception of digital competence was at a high level, and of the seven competence areas, values, ethics, and security, digital resources, and continuing professional development areas ranked among the top four, respectively, based on their means. Significant differences were observed in the digital competence of teachers with regard to different levels of education, disciplines, and institutional categories. Second, according to the results of structural equation modeling, facilitating conditions were positively and significantly correlated with teachers’ self-efficacy and digital competence. In addition, self-efficacy exerted a significantly positive impact on teachers’ digital competence. Finally, self-efficacy was found to mediate the relationship between facilitating conditions and teachers’ digital competence. Implications are suggested for developing teachers’ digital competence according to the findings of this study.
Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)

Earthquakes threaten people, homes, and infrastructure. Earthquake detection is a complex task because it does not show any specific pattern, unlike object detection from images. Convolutional neural networks have been widely used for earthquake detection but have problems like vanishing gradients, exploding, and parameter optimization. The ensemble learning approach combines multiple models, each of which attempts to compensate for the shortcomings of the others to enhance performance. This article proposes an ensemble learning model based on a stacked normalized recurrent neural network (SNRNN) for earthquake detection. The proposed model uses three recurrent neural network models (RNN, GRU, and LSTM) with batch normalization and layer normalization. After preprocessing the waveform data, the RNN, GRU, and LSTM extract the feature map sequentially. Batch normalization and layer normalization methods take place in mini-batches and input layers for stable and faster training of the model and improving its performance. We trained and tested the proposed model on 6574 events from 2000 to 2018 (18 years) in Turkey, a highly targeted region. The SNRNN achieves RMSE values of 3.16 and 3.24 for magnitude and depth detection. The SNRNN model outperforms the three baseline models, as seen by their low RMSE values.
Lightweight image super-resolution based multi-order gated aggregation network

Recently, Transformer-based models are taken much focus on solving the task of image super-resolution (SR) due to their ability to achieve better performance. However, these models combined huge computational cost during the computing self-attention mechanism. To solve this problem, we proposed a multi-order gated aggregation super-resolution network (MogaSRN) for low-level vision based on the concept of the MogaNet that is developed for high-level vision. The concept of the MogaSRN model is based on spatial multi-order context aggregation and adaptive channel-wise reallocation with the aid of the multi-layer perceptron (MLP). In contrast to the MogaNet model, in which the resolution of each stage decreased by a factor of 2, the resolution of the MogaSRN is stayed fixed during the deep features extraction. Moreover, the structure of the MogaSRN model is built based on balancing the performance and the model complexity. We evaluated our model based on five benchmark datasets concluding that the MogaSRN model can achieve significant improvements compared to the state-of-the-art. Moreover, our model shows the good visual quality and accuracy of the reconstruction. Finally, our model has 3.7 × faster runtime at the scale of × 4 compared to LWSwinIR with better performance.
Detach and unite: A simple meta-transfer for few-shot learning

Few-shot Learning (FSL) is a challenging problem that aims to learn and generalize from limited examples. Recent works have adopted a combination of meta-learning and transfer learning strategies for FSL tasks. These methods perform pre-training and transfer the learned knowledge to meta-learning. However, it remains unclear whether this transfer pattern is appropriate, and the objectives of the two learning strategies have not been explored. In addition, the inference of meta-learning in FSL relies on sample relations that require further consideration. In this paper, we uncover an overlooked discrepancy in learning objectives between pre-training and meta-learning strategies and propose a simple yet effective learning paradigm for the few-shot classification task. Specifically, the proposed method comprises two components: (i) Detach: We formulate an effective learning paradigm, Adaptive Meta-Transfer (A-MET), which adaptively eliminates undesired representations learned by pre-training to address the discrepancy. (ii) Unite: We propose a Global Similarity Compatibility Measure (GSCM) to jointly consider sample correlation at a global level for more consistent predictions. The proposed method is simple to implement without any complex components. Extensive experiments on four public benchmarks demonstrate that our method outperforms other state-of-the-art methods under more challenging scenarios with large domain differences between the base and novel classes and less support information available. Code is available at: https://github.com/yaoyz96/a-met.
Understanding Bugs in Multi-Language Deep Learning Frameworks

Deep learning frameworks (DLFs) have been playing an increasingly important role in this intelligence age since they act as a basic infrastructure for an increasingly wide range of AI-based applications. Meanwhile, as multi-programming-language (MPL) software systems, DLFs are inevitably suffering from bugs caused by the use of multiple programming languages (PLs). Hence, it is of paramount significance to understand the bugs (especially the bugs involving multiple PLs, i.e., MPL bugs) of DLFs, which can provide a foundation for preventing, detecting, and resolving bugs in the development of DLFs. To this end, we manually analyzed 1497 bugs in three MPL DLFs, namely MXNet, PyTorch, and TensorFlow. First, we classified bugs in these DLFs into 12 types (e.g., algorithm design bugs and memory bugs) according to their bug labels and characteristics. Second, we further explored the impacts of different bug types on the development of DLFs, and found that deployment bugs and memory bugs negatively impact the development of DLFs in different aspects the most. Third, we found that 28.6%, 31.4%, and 16.0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs. Finally, the code change complexity of MPL bug fixes is significantly greater than that of single-programming-language (SPL) bug fixes in all the three DLFs, while in PyTorch MPL bug fixes have longer open time and greater communication complexity than SPL bug fixes. These results provide insights for bug management in DLFs.
Automatic Aug-Aware Contrastive Proposal Encoding for Few-Shot Object Detection of Remote Sensing Images

In the annotation of remote sensing images (RSIs), the effectiveness of common object detection methods trained on only a few samples decreases instantly, which has prompted increasing research on the few-shot problem in remote sensing. RSIs often exhibit suboptimal performance in few-shot scenarios due to the intricate nature of scene information interference and the high degree of cosine similarity, both of which present significant challenges to their effectiveness. In this paper, a two-stage detection framework based on fine-tuning is selected to deal with the common problems in the few-shot task of remote sensing domain. Considering the excessive scale variation of instances in remote sensing datasets, we introduce an automatically learned aug-aware search module to provide an intelligent data augmentation solution for Faster R-CNN using different optimal augmentation policies searched by the network to fit the current dataset. We introduce a contrastive RoI branch to better classify novel class proposal features that are easily confused by the base class. We named our work AACE and conducted extensive experiments on two common object detection datasets in remote sensing, NWPU VHR 10 and DIOR, on which AACE achieved about 2.30% and 2.61% improvement, respectively, in the number of shots listed in the paper, compared to other algorithms.