Reviewerly/exHarmony huggingface dataset:
Customized Curriculum and Learning Approach Recommendation Techniques in Application of Virtual Reality in Medical Education

Virtual Reality (VR) has made considerable gains in the consumer and professional markets. As VR has progressed as a technology, its overall usefulness for educational purposes has grown. On the other hand, the educational field struggles to keep up with the latest innovations, changing affordances, and pedagogical applications due to the rapid evolution of technology. Therefore, many have elaborated on the potential of virtual reality (VR) in learning. This research proposes a novel techniques customized curriculum for medical students and recommendations for their learning process based on deep learning techniques. Here the data has been collected based on the pre-historic performance of the student and their current requirement and these data have been created as a dataset. Then this has been processed for analysis based on CAD system integrated with deep learning techniques for creating a customized curriculum. Initially this data has been processed and analysed to remove missing and invalid data. Then these data were classified for creation of the curriculum using a gradient decision tree integrated&amp;nbsp;with na&amp;iuml;ve Bayes. From this, the customized curriculum has been generated. Based on this customized curriculum, the learning approach recommendation has been carried out using the fuzzy rules integrated knowledge-based recommendation system. The experimental results of the proposed technique have been carried out with an accuracy of 98%, specificity of 82%, F-1 score of 79%, information overload of 75%, and precision of 81%.
Development of Optimal Tilt Angle Models of a Photovoltaic Module for Maximum Power Production: Ethiopia

The power generated from the photovoltaic module is directly related to the magnitude of total incident solar radiation on the surface of the solar module. The total incident solar radiation depends on the location, tilt angle, and orientation of the solar module. In this paper, generic models were developed that determine the seasonal and annual optimal tilt angle of the Photovoltaic module at any location in Ethiopia without using meteorological data. Both isotropic and anisotropic diffuse solar radiation models were used to estimate monthly, seasonal, and annual optimal tilt angles. The monthly average daily global horizontal solar radiation for a total of 44 cities -32 for training and 12 for testing were obtained from the National Aeronautical and Space Administration database, and algorithms were developed and implemented using MATLAB and R programming software to obtain optimum tilt angle and regression models. The study showed that the developed model accurately estimates the optimal tilt angle with the minimum statistical validation errors. It is also found that 5.11% to 6.275% (isotropic) and 5.72% to 6.346% (anisotropic models) solar radiation energy is lost when using the yearly average fixed optimal tilt angle as compared with the monthly optimal tilt angle. The result of this study was also validated by comparing it with the previously published works, PVGIS and PVWatt online software. The graphical abstract is included in the supplementary file.
An improved extreme learning machine with self-recurrent hidden layer

Extreme learning machine (ELM) is widely used in complex industrial problems, especially the online-sequential extreme learning machine (OS-ELM) plays a good role in industrial online modeling. However, OS-ELM requires batch samples to be pre-trained to obtain initial weights, which may reduce the timeliness of samples. This paper proposes a novel model for the online process regression prediction, which is called the Recurrent Extreme Learning Machine (Recurrent-ELM). The nodes between the hidden layers are connected in Recurrent-ELM, thus the input of the hidden layer receives both the information from the current input layer and the previously hidden layer. Moreover, the weights and biases of the proposed model are generated by analysis rather than random. Six regression applications are used to verify the designed Recurrent-ELM, compared with extreme learning machine (ELM), fast learning network (FLN), online sequential extreme learning machine (OS-ELM), and an ensemble of online sequential extreme learning machine (EOS-ELM), the experimental results show that the Recurrent-ELM has better generalization and stability in several samples. In addition, to further test the performance of Recurrent-ELM, we employ it in the combustion modeling of a 330 MW coal-fired boiler compared with FLN, SVR and OS-ELM. The results show that Recurrent-ELM has better accuracy and generalization ability, and the theoretical model has some potential application value in practical application.
Strengthening government’s response to COVID-19 in Indonesia: A modified Delphi study of medical and health academics

The Indonesian government has issued various policies to control COVID-19. However, COVID-19 new cases continued to increase, and there remain uncertainties as to the future trajectory. We aimed to investigate how medical and health academics view the Indonesian government’s handling of COVID-19 and which areas of health systems need to be prioritized to improve the government’s response to COVID-19. We conducted a modified Delphi study adapting the COVID-19 assessment scorecard (COVID-SCORE) as the measurement criteria. We invited medical and health academics from ten universities across Indonesia to take part in the two-round Delphi study. In the first round, participants were presented with 20 statements of COVID-SCORE and asked to rate their agreement with each statement using a five-point Likert scale. All participants who completed the first cycle were invited to participate in the second cycle. They had the opportunity to revise their answers based on the previous cycle’s results and ranked a list of actions to improve government response. We achieved a moderate consensus level for five statements, a low consensus level for 13 statements and no consensus for two statements. The prioritization suggested that top priorities for improving the government’s response to COVID-19 in Indonesia encompass: (1) strengthening capacity to ensure consistent, credible and targeted communication while adopting a more inclusive and empathic communication style to address public concerns; (2) ensuring universal access to reliable COVID-19 testing by expanding lab infrastructure, facilitating operational readiness, and scaling up implementation of proven alternative/complementary tests to RT-PCR; and (3) boosting contact tracing implementation capacity and facilitating contact tracing for all positive cases, involving key stakeholders in further development of the existing contact tracing system (i.e. PeduliLindungi) as well as its evaluation and quality assurance. Ultimately, our study highlights the importance of strengthening health system functions during the pandemic and improving health system resilience for dealing with future public health emergencies.
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study

For the most comfortable, human-aware robot navigation, subjective user preferences need to be taken into account. This paper presents a novel reinforcement learning framework to train a personalized navigation controller along with an intuitive virtual reality demonstration interface. The conducted user study provides evidence that our personalized approach significantly outperforms classical approaches with more comfortable human-robot experiences. We achieve these results using only a few demonstration trajectories from non-expert users, who predominantly appreciate the intuitive demonstration setup. As we show in the experiments, the learned controller generalizes well to states not covered in the demonstration data, while still reflecting user preferences during navigation. Finally, we transfer the navigation controller without loss in performance to a real robot.
Toward Code Review Notebooks

Peer code review has proven to be a valuable tool in software engineering.However, integrating code reviews into educational contexts is particularly challenging due to the complexity of both the process and popular code review tools.We propose to address this challenge by designing a code review application (CRA) aimed at teaching the code review process directly within existing online learning platforms.Using the CRA, instructors can scaffold online lessons that introduce the code review process to students through code snippets, following a format resembling computational notebooks.We refer to this online lesson format as the code review notebook format.Through a case study comprising an online lesson on code quality standards completed by 23 university students, we evaluated the usability of the CRA and the code review notebook format, obtaining positive results for both.These results are a first step toward integrating code review notebooks into software engineering education.
[Spatial Distribution, Source Apportionment, and Ecological Risk Assessment of Soil Heavy Metals in Jianghugongmi Producing Area, Shandong Province].

Taking the Jianghugongmi producing area as the research object, As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the soil of the study area were sampled and determined. The correlation of heavy metals was discussed using the multivariate statistical method, the spatial distribution interpolation analysis of heavy metals was carried out using ArcGIS 10.2, the quantitative source analysis of heavy metal pollution was carried out using the enrichment factor (EF) and PMF methods, and the potential ecological risk was evaluated. The results showed that the contents of the soil heavy metals As, Cd, Cu, Hg, Pb, and Zn were lower than the screening values specified in the standard for soil pollution risk control of agricultural land (GB 15618-2018), and the soil ecological environment risk was low; the maximum values of Cr and Ni exceeded the risk screening values, but the risk was low. The main distribution range of pH in the study area was 6.05-6.69, which was suitable for rice growth. The Mohe River indicated the spatial distribution of pH and heavy metals, which was closely related to the supergene geochemical characteristics of the elements. However, Hg and Cd showed different spatial distribution characteristics under human influence. Hg was distributed in the middle and high value distribution area along the west side of the river, and the spatial distribution of Cd was significantly different from north to south. The quantitative source analysis results based on the EF method and PMF showed that the main sources of heavy metals in the study area were agricultural sources, mixed sources, coal sources, and natural sources. The contribution rates of various sources accounted for 24.2%, 35.4%, 9.5%, and 30.9%, respectively. The medium strong ecological risk points of Hg in the study area were distributed along the west side of the Mohe River, whereas the moderate potential ecological risk points of Cd were concentrated in the cultivated land on both sides of the Mohe River, and the potential ecological risk index (Er) of the other elements was<40. Cd and Hg were the main potential ecological risk elements in the study area, whereas Cd was still the main potential pollution element in the cultivated land soil in the study area.
Comparisons Where It Matters: Using Layer-Wise Regularization to Improve Federated Learning on Heterogeneous Data

Federated Learning is a widely adopted method for training neural networks over distributed data. One main limitation is the performance degradation that occurs when data are heterogeneously distributed. While many studies have attempted to address this problem, a more recent understanding of neural networks provides insight to an alternative approach. In this study, we show that only certain important layers in a neural network require regularization for effective training. We additionally verify that Centered Kernel Alignment (CKA) most accurately calculates similarities between layers of neural networks trained on different data. By applying CKA-based regularization to important layers during training, we significantly improved performances in heterogeneous settings. We present FedCKA, a simple framework that outperforms previous state-of-the-art methods on various deep learning tasks while also improving efficiency and scalability.
Cross-task Attention Mechanism for Dense Multi-task Learning

Multi-task learning has recently become a promising solution for comprehensive understanding of complex scenes. With an appropriate design, multi-task models can not only be memory-efficient but also favour the exchange of complementary signals across tasks. In this work, we jointly address 2D semantic segmentation, and two geometry-related tasks, namely dense depth, surface normal estimation as well as edge estimation showing their benefit on several datasets. We propose a novel multi-task learning architecture that exploits pair-wise cross-task exchange through correlation-guided attention and self-attention to enhance the average representation learning for all tasks. We conduct extensive experiments on three multi-task setups, showing the benefit of our proposal in comparison to competitive baselines in both synthetic and real benchmarks. We also extend our method to the novel multi-task unsupervised domain adaptation setting. Our code is available at https://github.com/cv-rits/DenseMTL
You Can even Annotate Text with Voice: Transcription-only-Supervised Text Spotting

End-to-end scene text spotting has recently gained great attention in the research community. The majority of existing methods rely heavily on the location annotations of text instances (e.g., word-level boxes, word-level masks, and char-level boxes). We demonstrate that scene text spotting can be accomplished solely via text transcription, significantly reducing the need for costly location annotations. We propose a query-based paradigm to learn implicit location features via the interaction of text queries and image embeddings. These features are then made explicit during the text recognition stage via an attention activation map. Due to the difficulty of training the weakly-supervised model from scratch, we address the issue of model convergence via a circular curriculum learning strategy. Additionally, we propose a coarse-to-fine cross-attention localization mechanism for more precisely locating text instances. Notably, we provide a solution for text spotting via audio annotation, which further reduces the time required for annotation. Moreover, it establishes a link between audio, text, and image modalities in scene text spotting. Using only transcription annotations as supervision on both real and synthetic data, we achieve competitive results on several popular scene text benchmarks. The proposed method offers a reasonable trade-off between model accuracy and annotation time, allowing simplification of large-scale text spotting applications.
A coverless image steganography based on robust image wavelet hashing

Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. In this paper, a new coverless steganography is presented to hide the secret data in a more secure way and to enhance the robustness against attacks. This method depends on frequency domain. The embedding process consists of several steps. Firstly, the secret data is divided into no overlapping segments. Secondly, a set of images is collected to find appropriate images to be stego images. Thirdly, to build a hash sequence for an image, a powerful hashing algorithm is used. Fourthly, for each image hash sequence, the inverted index structure is created. Fifthly, choose the image which its hash equivalent to the secret data segment. Several tests are done to measure the robustness of the proposed method. The results of the experiments reveal that the proposed strategy is resistant to a variety of image processing attacks such as joint photographic experts group (JPEG) compression, noise, low pass filtering, scaling, rotation and median and mean filter, brightness, and sharpening.
Making The Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation

Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the deployment of deep learning from limited experimental datasets into real-world unconstrained domains. Most UDA approaches align features within a common embedding space and apply a shared classifier for target prediction. However, since a perfectly aligned feature space may not exist when the domain discrepancy is large, these methods suffer from two limitations. First, the coercive domain alignment deteriorates target domain discriminability due to lacking target label supervision. Second, the source-supervised classifier is inevitably biased to source data, thus it may underperform in target domain. To alleviate these issues, we propose to simultaneously conduct feature alignment in two individual spaces focusing on different domains, and create for each space a domain-oriented classifier tailored specifically for that domain. Specifically, we design a Domain-Oriented Transformer (DOT) that has two individual classification tokens to learn different domain-oriented representations, and two classifiers to preserve domain-wise discriminability. Theoretical guaranteed contrastive-based alignment and the source-guided pseudo-label refinement strategy are utilized to explore both domain-invariant and specific information. Comprehensive experiments validate that our method achieves state-of-the-art on several benchmarks. Code is released at https://github.com/BIT-DA/Domain-Oriented-Transformer.
Solar Forecasts Based on the Clear Sky Index or the Clearness Index: Which Is Better?

In the realm of solar forecasting, it is common to use a clear sky model output to deseasonalise the solar irradiance time series needed to build the forecasting models. However, most of these clear sky models require the setting of atmospheric parameters for which accurate values may not be available for the site under study. This can hamper the accuracy of the prediction models. Normalisation of the irradiance data with a clear sky model leads to the construction of forecasting models with the so-called clear sky index. Another way to normalize the irradiance data is to rely on the extraterrestrial irradiance, which is the irradiance at the top of the atmosphere. Extraterrestrial irradiance is defined by a simple equation that is related to the geometric course of the sun. Normalisation with the extraterrestrial irradiance leads to the building of models with the clearness index. In the solar forecasting domain, most models are built using time series based on the clear sky index. However, there is no empirical evidence thus far that the clear sky index approach outperforms the clearness index approach. Therefore the goal of this preliminary study is to evaluate and compare the two approaches. The numerical experimental setup for evaluating the two approaches is based on three forecasting methods, namely, a simple persistence model, a linear AutoRegressive (AR) model, and a non-linear neural network (NN) model, all of which are applied at six sites with different sky conditions. It is shown that normalization of the solar irradiance with the help of a clear sky model produces better forecasts irrespective of the type of model used. However, it is demonstrated that a nonlinear forecasting technique such as a neural network built with clearness time series can beat simple linear models constructed with the clear sky index.
Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

A reinforcement learning-based continuous action space path planning method for mobile robots is proposed in this article. First, the kinematic model of the mobile robot is analyzed, and on this basis, the optimal state space is constructed according to the minimum depth of the field value in the depth image to characterize the distance between the robot and the obstacle. Then, by setting the reward function of the mobile robot based on the artificial potential field method, the information of the robot’s distance from obstacles is continuous, and a new reinforcement learning training process is proposed. Finally, by introducing a DDPG algorithm, the path planning of a mobile robot in an unknown environment is described as a Markov decision process, and the optimal planning of the mobile robot’s continuous action space path is realized with a high success rate. The results show that compared with other three comparison methods, the final success rates of the proposed method are the highest, which are 97.2%, 99.1%, 98.4%, and 98.6%, respectively.
Binary Dense SIFT Flow Based Position-Information Added Two-Stream CNN for Pedestrian Action Recognition

Pedestrian behavior recognition in the driving environment is an important technology to prevent pedestrian accidents by predicting the next movement. It is necessary to recognize current pedestrian behavior to predict future pedestrian behavior. However, many studies have recognized human visible characteristics such as face, body parts or clothes, but few have recognized pedestrian behavior. It is challenging to recognize pedestrian behavior in the driving environment due to the changes in the camera field of view due to the illumination conditions in outdoor environments and vehicle movement. In this paper, to predict pedestrian behavior, we introduce a position-information added two-stream convolutional neural network (CNN) with multi task learning that is robust to the limited conditions of the outdoor driving environment. The conventional two-stream CNN is the most widely used model for human-action recognition. However, the conventional two-stream CNN based on optical flow has limitations regarding pedestrian behavior recognition in a moving vehicle because of the assumptions of brightness constancy and piecewise smoothness. To solve this problem for a moving vehicle, the binary descriptor dense scale-invariant feature transform (SIFT) flow, a feature-based matching algorithm, is robust in moving-pedestrian behavior recognition, such as walking and standing, in a moving vehicle. However, recognizing cross attributes, such as crossing or not crossing the street, is challenging using the binary descriptor dense SIFT flow because people who cross the road or not act the same walking action, but their location on the image is different. Therefore, pedestrian position information should be added to the conventional binary descriptor dense SIFT flow two-stream CNN. Thus, learning biased toward action attributes is evenly learned across action and cross attributes. In addition, YOLO detection and the Siamese tracker are used instead of the ground-truth boundary box to prove the robustness in the action- and cross-attribute recognition from a moving vehicle. The JAAD and PIE datasets were used for training, and only the JAAD dataset was used as a testing dataset for comparison with other state-of-the-art research on multitask and single-task learning.