Reviewerly/exHarmony huggingface dataset:
Forecasting of Solar Energy Generation via Dynamic Model Ensemble

Solar energy is one of the primary renewable energy sources. The usage of solar energy generation can help reduce carbon emissions and thus help address the challenge of global climate change. In the power system, we would like to maintain the balance between power generation and power usage. How-ever, solar energy generation is quite intermittent and will be affected by many uncontrollable factors, such as temperature, cloud cover, and humidity levels. Hence, the accurate forecasting of solar energy generation is of significant importance for the secure operation of power grids. Different types of methods have been developed for solar power generation forecasting, including statistical methods and machine learning methods. In this work, we propose a hybrid dynamic ensemble framework for solar energy generation forecasting. Specifically, a set of base forecasting learners will be first learned. Then, a set of weights will be dynamically updated for the base learners based on the expectation of the contributions of the base learners. Extensive experiment results on the real-world dataset have been implemented and demonstrated the effectiveness and robustness of our proposed method. The proposed forecasting method consistently outperforms all single-model baselines and the static ensemble model.
Use of Journey Maps and Personas in Software Requirements Elicitation

Requirements elicitation is a fundamental step in a software development process since it is at this stage that the software begins to be designed. In some situations, the problems related to the failure of the software development project are due to an incomplete requirements elicitation, resulting in solutions that do not understand all the necessary functionalities or do not incorporate innovation. Despite the various techniques offered by Requirements Engineering, situations such as the growing application market and the need for innovation further increase the importance of understanding the user’s different needs. In this paper, we investigated how the journey map and personas techniques are being used in requirements elicitation in both the literature and the industry, along with the advantages, disadvantages and challenges of using these techniques. We conducted systematic literature review to identify the personas and journey map techniques used in requirements elicitation in the literature and industry. In addition, we conducted a survey with 52 practitioners (software developers, users and managers) to investigate their perceptions of the use of journey maps and personas techniques in the requirements elicitation phase. Twenty-four primary studies were identified that address journey map and personas techniques in software requirements elicitation. In addition, most respondents stated that using these techniques facilitates understanding the requirements, providing better integration, collaboration and leveling of knowledge among the members of the software development teams. Our findings allow us to conclude that most of the software developers, users and managers that participated in the survey consider that the journey map and personas techniques are effective in helping understand the software requirements to be developed by the development teams.
AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models

The objective of pre-trained language models is to learn contextual representations of textual data. Pre-trained language models have become mainstream in natural language processing and code modeling. Using probes, a technique to study the linguistic properties of hidden vector spaces, previous works have shown that these pre-trained language models encode simple linguistic properties in their hidden representations. However, none of the previous work assessed whether these models encode the whole grammatical structure of a programming language. In this paper, we prove the existence of a syntactic subspace, lying in the hidden representations of pre-trained language models, which contain the syntactic information of the programming language. We show that this subspace can be extracted from the models' representations and define a novel probing method, the AST-Probe, that enables recovering the whole abstract syntax tree (AST) of an input code snippet. In our experimentations, we show that this syntactic subspace exists in five state-of-the-art pre-trained language models. In addition, we highlight that the middle layers of the models are the ones that encode most of the AST information. Finally, we estimate the optimal size of this syntactic subspace and show that its dimension is substantially lower than those of the models' representation spaces. This suggests that pre-trained language models use a small part of their representation spaces to encode syntactic information of the programming languages.
LiveRef: a Tool for Live Refactoring Java Code

Refactoring software can be hard and time-consuming. Several refactoring tools assist developers in reaching more readable and maintainable code. However, most of them are characterized by long feedback loops that impoverish their refactoring experience. We believe that we can reduce this problem by focusing on the concept of Live Refactoring and its main principles: the live recommendation and continuous visualization of refactoring candidates, and the immediate visualization of results from applying a refactoring to the code. Therefore, we implemented a Live Refactoring Environment that identifies, suggests, and applies Extract Method refactorings. To evaluate our approach, we carried out an empirical experiment. Early results showed us that our refactoring environment improves several code quality aspects, being well received, understood, and used by the experiment participants. The source code of our tool is available on: https://github.com/saracouto1318/LiveRef. Its demonstration video can be found at: https://youtu.be/_jxx21ZiQ0o.
The Use of Digital Resources in Teaching during the Pandemic: What Type of Learning Have They Promoted?

The COVID-19 pandemic induced an accelerated transition to digital teaching in all countries. We ask ourselves whether this massive use of digital resources promoted student–centred, dialogical, and multimodal teaching, as shown in some experimental studies, or whether, on the contrary, these resources were used only as a substitute for the teacher’s voice, maintaining content-centred teaching. We analysed 269 activities carried out during the lockdown by teachers in Spain through the System of Analysis of Teaching Activities. This analysis system considered the resources used and the learning they promoted within activities. In general, the activities were content-centred independently of the resource used. However, in a few cases, activities were student–centred. The greatest systematic difference between the two types of activities was related to who managed these resources. Only when the student managed the digital resources were the activities student-centred. Conversely, when the tasks were content-centred, teachers managed the resources. These results indicate the need to achieve proven ICT integration in education, which in turn requires a boost in teacher training aimed at both familiarising teachers with the use of digital resources and, above all, promoting a change in teachers’ conceptions about their use.
TRNR: Task-Driven Image Rain and Noise Removal With a Few Images Based on Patch Analysis

The recent success of learning-based image rain and noise removal can be attributed primarily to well-designed neural network architectures and large labeled datasets. However, we discover that current image rain and noise removal methods result in low utilization of images. To alleviate the reliance of deep models on large labeled datasets, we propose the task-driven image rain and noise removal (TRNR) based on a patch analysis strategy. The patch analysis strategy samples image patches with various spatial and statistical properties for training and can increase image utilization. Furthermore, the patch analysis strategy encourages us to introduce the N-frequency-K-shot learning task for the task-driven approach TRNR. TRNR allows neural networks to learn from numerous N-frequency-K-shot learning tasks, rather than from a large amount of data. To verify the effectiveness of TRNR, we build a Multi-Scale Residual Network (MSResNet) for both image rain removal and Gaussian noise removal. Specifically, we train MSResNet for image rain removal and noise removal with a few images (for example, 20.0% train-set of Rain100H). Experimental results demonstrate that TRNR enables MSResNet to learn more effectively when data is scarce. TRNR has also been shown in experiments to improve the performance of existing methods. Furthermore, MSResNet trained with a few images using TRNR outperforms most recent deep learning methods trained data-driven on large labeled datasets. These experimental results have confirmed the effectiveness and superiority of the proposed TRNR. The source code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Schizophreni/MSResNet-TRNR</uri> .
STFE-Net: A Spatial-Temporal Feature Extraction Network for Continuous Sign Language Translation

The main challenge of continuous sign language translation (CSLT) lies in the extraction of both discriminative spatial features and temporal features. In this paper, a spatial-temporal feature extraction network (STFE-Net) is proposed for CSLT, which optimally fuses spatial and temporal features, extracted by the spatial feature extraction network (SFE-Net) and the temporal feature extraction network (TFE-Net), respectively. SFE-Net performs pose estimation for the presenters in sign-language videos. Based on COCO-WholeBody, 133 key points are abbreviated to 53 key points, according to the characteristics of the sign language. High-resolution pose estimation is performed on the hands, along with the whole-body pose estimation, to obtain finer-grained hand features. The spatial features extracted by SFE-Net and the sign language words are then fed to TFE-Net, which is based on Transformer with relative position encoding. In this paper, a dataset for Chinese continuous sign language was created and used for evaluation. STFE-Net achieves Bilingual Evaluation Understudy (BLEU-1, BLEU-2, BLEU-3, BLEU-4) scores of 77.59, 75.62, 74.25, 72.14, respectively. Furthermore, our proposed STFE-Net was also evaluated on two public datasets, RWTH-Phoenix-Weather 2014T and CLS. The BLEU-1, BLEU-2, BLEU-3 and BLEU-4 scores achieved by our method on the former dataset are 48.22, 33.59, 26.41 and 22.45, respectively, and the corresponding scores are 61.54, 58.76, 57.93 and 57.52, respectively, on the latter dataset. Experiment results show that our model can achieve promising performance. If any reader needs the code or dataset, please email lunfee@whut.edu.cn.
Domain Adaptive Transformer Tracking Under Occlusions

Due to their excellent performance on aggregating global features, Transformer structures are being widely employed in deep learning-based visual object tracking algorithms, recently. Nevertheless, existing Transformer-based trackers still fail to handle occlusion problems due to drift in feature distributions. To address this issue, we introduce domain adaptation techniques into a novel object tracking framework, DATransT, including feature extraction, domain adaptive Transformer module and prediction head. The domain adaptive Transformer module consists of three weight-sharing branches with self and cross attention mechanisms: the source, the target and the source-target branches. Specifically, the source-target branch employs cross-attention to effectively align the feature distributions of the source and target branches. Meanwhile, we present a pseudo-labeling strategy to generate high-quality training samples. Extensive experiments show that DATransT obtains promising results on several popular datasets, containing LaSOT, TrackingNet, GOT-10k, NfS, OTB2015 and UAV123. Moreover, our method outperforms existing state-of-the-art trackers under full occlusions and partial occlusions.
OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.
Visual question answering model for fruit tree disease decision-making based on multimodal deep learning

Visual Question Answering (VQA) about diseases is an essential feature of intelligent management in smart agriculture. Currently, research on fruit tree diseases using deep learning mainly uses single-source data information, such as visible images or spectral data, yielding classification and identification results that cannot be directly used in practical agricultural decision-making. In this study, a VQA model for fruit tree diseases based on multimodal feature fusion was designed. Fusing images and Q&A knowledge of disease management, the model obtains the decision-making answer by querying questions about fruit tree disease images to find relevant disease image regions. The main contributions of this study were as follows: (1) a multimodal bilinear factorized pooling model using Tucker decomposition was proposed to fuse the image features with question features: (2) a deep modular co-attention architecture was explored to simultaneously learn the image and question attention to obtain richer graphical features and interactivity. The experiments showed that the proposed unified model combining the bilinear model and co-attentive learning in a new network architecture obtained 86.36% accuracy in decision-making under the condition of limited data (8,450 images and 4,560k Q&A pairs of data), outperforming existing multimodal methods. The data augmentation is adopted on the training set to avoid overfitting. Ten runs of 10-fold cross-validation are used to report the unbiased performance. The proposed multimodal fusion model achieved friendly interaction and fine-grained identification and decision-making performance. Thus, the model can be widely deployed in intelligent agriculture.
Probabilistic Intraday PV Power Forecast Using Ensembles of Deep Gaussian Mixture Density Networks

There is a growing interest of estimating the inherent uncertainty of photovoltaic (PV) power forecasts with probability forecasting methods to mitigate accompanying risks for system operators. This study aims to advance the field of probabilistic PV power forecast by introducing and extending deep Gaussian mixture density networks (MDNs). Using the sum of the weighted negative log likelihood of multiple Gaussian distributions as a minimizing objective, MDNs can estimate flexible uncertainty distributions with nearly all neural network structures. Thus, the advantages of advances in machine learning, in this case deep neural networks, can be exploited. To account for the epistemic (e.g., model) uncertainty as well, this study applies two ensemble approaches to MDNs. This is particularly relevant for industrial applications, as there is often no extensive (manual) adjustment of the forecast model structure for each site, and only a limited amount of training data are available during commissioning. The results of this study suggest that already seven days of training data are sufficient to generate significant improvements of 23.9% in forecasting quality measured by normalized continuous ranked probability score (NCRPS) compared to the reference case. Furthermore, the use of multiple Gaussian distributions and ensembles increases the forecast quality relatively by up to 20.5% and 19.5%, respectively.
A Holistic Approach for Design and Assessment of Building-Integrated Photovoltaics Systems

This article addresses the application of building-integrated photovoltaic (BIPV) systems through the analysis of a case study with different operating conditions and geospatial locations. The research is carried out with customer-made photovoltaic modules supported by computational aids. The results obtained from real-life BIPV installation are contrasted, simulated, and improved through different scenarios where aspects such as software deviation, shadowing influence, installed capacity, and project profitability are analyzed to establish viability pathways for BIPV projects. As a result, the most relevant factors to improve the technical and economic conditions of the BIPV system are the total capacity installed, the use of the available area, and the strategic location of the modules to avoid shading surfaces. In this way, this work addresses the analysis of BIPV systems through the assessment of a case study implemented in a real residential structure in Colombia. The proposed methodology includes simulations to evaluate the solar energy potential considering the elements in the neighborhood of the BIPV system and technical aspects, such as the wiring and power interface, an economical study to find the feasibility of the project, and an analysis of different operating scenarios. As a result, the most important factors that affect the operation of BIPV systems under Colombian weather conditions were identified: total installed capacity, use of the available area, and strategic location of the modules to avoid shading surfaces. Such factors can then be considered in the early stage of designing for future BIPV applications.
A Method for Calculating the Derivative of Activation Functions Based on Piecewise Linear Approximation

Nonlinear functions are widely used as activation functions in artificial neural networks, which have a great impact on the fitting ability of artificial neural networks. Due to the complexity of the activation function, the computation of the activation function and its derivative requires a lot of computing resources and time during training. In order to improve the computational efficiency of the derivatives of the activation function in the back-propagation of artificial neural networks, this paper proposes a method based on piecewise linear approximation method to calculate the derivative of the activation function. This method is hardware-friendly and universal, it can efficiently compute various nonlinear activation functions in the field of neural network hardware accelerators. In this paper, we use least squares to improve a piecewise linear approximation calculation method that can control the absolute error and get less number of segments or smaller average error, which means fewer hardware resources are required. We use this method to perform a segmented linear approximation to the original or derivative function of the activation function. Both types of activation functions are substituted into a multilayer perceptron for binary classification experiments to verify the effectiveness of the proposed method. Experimental results show that the same or even slightly higher classification accuracy can be achieved by using this method, and the computation time of the back-propagation is reduced by 4–6% compared to the direct calculation of the derivative directly from the function expression using the operator encapsulated in PyTorch. This shows that the proposed method provides an efficient solution of nonlinear activation functions for hardware acceleration of neural networks.
Comparison of Different Methods of Animal Detection and Recognition on Thermal Camera Images

For most wild animals, the peak activity occurs during the night hours—their observation is possible only with the use of specialized equipment. Advancements in technology and the availability of thermal sensing devices allow researchers to examine wildlife. This study compares different methods for animal detection in thermal camera images including classical (HOG/SVM) and based on deep neural networks (Faster RCNN and YOLO). A comparison was performed to indicate the most beneficial mean Average Precision (mAP) for different levels of Intersection over Union (IoU) coverage thresholds and sensitivity (Recall). The results were evaluated on a scratch dataset containing two animal families (Cervidae and Suidae). The models were compared in terms of precision, recall, and training time. After a series of tests, the achieved performance was very satisfying: for the YOLOv3 network, the obtained mAP was above 90% for IoU &gt; 50%; for Faster R-CNN, the obtained mAP was 87%; and for HOG/SVM, the obtained mAP was 40%. The training time for HOG/SVM was under 1 min, for YOLOv3 it was around 50 min, and for Faster R-CNN it was around 60 min. This research provides an important starting point for the creation of a ground-based system for autonomous observation of migration and population numbers, which is a unique approach in the field of wildlife research.
Semi−Supervised Hybrid Modeling of the Yeast Fermentation Process

This study focuses on modeling the yeast fermentation process using the hybrid modeling method. To improve the prediction accuracy of the model and reduce the model training time, this paper presents a semi−supervised hybrid modeling method based on an extreme learning machine for the yeast fermentation process. The hybrid model is composed of the mechanism model and the residual model. The residual model is built from the residuals between the real yeast fermentation process and the mechanism model. The residual model is used in parallel with the mechanism model. Considering that the residuals might be related to the inaccurate parameters or structure of the process, the mechanism model output is taken as unlabeled data, and the suitable inputs are selected based on Pearson’s maximum correlation and minimum redundancy criterion (RRPC). Meanwhile, an extreme learning machine is employed to improve the model’s training speed while maintaining the model’s prediction accuracy. Consequently, the proposal proved its efficacy through simulation.