Reviewerly/exHarmony huggingface dataset:
A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial Images

Solar energy production has significantly increased in recent years in the European Union (EU), accounting for 12% of the total in 2022.The growth in solar energy production can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives.The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources.As a result, there is a growing need for efficient methods for detecting and mapping the locations of PV panels.Automated detection can in fact save time and resources compared to manual inspection.Moreover, the resulting information can also be used by governments, environmental agencies and other companies to track the adoption of renewable sources or to optimize energy distribution across the grid.However, building effective models to support the automated detection and mapping of solar photovoltaic (PV) panels presents several challenges, including the availability of high-resolution aerial imagery and high-quality, manually-verified labels and annotations.In this study, we address these challenges by first constructing a dataset of PV panels using very-high-resolution (VHR) aerial imagery, specifically focusing on the region of Piedmont in Italy.The dataset comprises 105 large-scale images, providing more than 9,000 accurate and detailed manual annotations, including additional attributes such as the PV panel category.We first conduct a comprehensive evaluation benchmark on the newly constructed dataset, adopting various well-established deep-learning techniques.Specifically, we experiment with instance and semantic segmentation approaches, such as Rotated Faster RCNN and Unet, comparing strengths and weaknesses on the task at hand.Second, we apply ad-hoc modifications to address the specific issues of this task, such as the wide range of scales of the installations and the sparsity of the annotations, considerably improving upon the baseline results.Last, we introduce a robust and efficient post-processing polygonization algorithm that is tailored to PV panels.This algorithm converts the rough raster predictions into cleaner and more precise polygons for practical use.Our benchmark evaluation shows that both semantic and instance segmentation techniques can be effective for detecting and mapping PV panels.Instance segmentation techniques are well-suited for estimating the localization of panels, while semantic solutions excel at surface delineation.We also demonstrate the effectiveness of our ad-hoc solutions and post-processing algorithm, which can provide an improvement up to +10% on the final scores, and can accurately convert coarse raster predictions into usable polygons.
Excellent predictive-performances of photonic reservoir computers for chaotic time-series using the fusion-prediction approach

In this work, based on two parallel reservoir computers realized by the two polarization components of the optically pumped spin-VCSEL with double optical feedbacks, we propose the fusion-prediction scheme for the Mackey-Glass (MG) and Lorenz (LZ) chaotic time series. Here, the direct prediction and iterative prediction results are fused in a weighted average way. Compared with the direct-prediction errors, the fusion-prediction errors appear great decrease. Their values are far less than the values of the direct-prediction errors when the iteration step-size are no more than 15. By the optimization of the temporal interval and the sampling period, under the iteration step-size of 3, the fusion-prediction errors for the MG and LZ chaotic time-series can be reduced to 0.00178 and 0.004627, which become 8.1% of the corresponding direct-prediction error and 28.68% of one, respectively. Even though the iteration step-size reaches to 15, the fusion-prediction errors for the MG and LZ chaotic time-series can be reduced to 55.61% of the corresponding direct-prediction error and 77.28% of one, respectively. In addition, the fusion-prediction errors have strong robustness on the perturbations of the system parameters. Our studied results can potentially apply in the improvement of prediction accuracy for some complex nonlinear time series.
Knowledge graph embedding methods for entity alignment: experimental review

Abstract In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A frequent task when integrating knowledge from different KGs is to find which subgraphs refer to the same real-world entity, a task largely known as the Entity Alignment. Recently, embedding methods have been used for entity alignment tasks, that learn a vector-space representation of entities which preserves their similarity in the original KGs. A wide variety of supervised, unsupervised, and semi-supervised methods have been proposed that exploit both factual (attribute based) and structural information (relation based) of entities in the KGs. Still, a quantitative assessment of their strengths and weaknesses in real-world KGs according to different performance metrics and KG characteristics is missing from the literature. In this work, we conduct the first meta-level analysis of popular embedding methods for entity alignment, based on a statistically sound methodology. Our analysis reveals statistically significant correlations of different embedding methods with various meta-features extracted by KGs and rank them in a statistically significant way according to their effectiveness across all real-world KGs of our testbed. Finally, we study interesting trade-offs in terms of methods’ effectiveness and efficiency.
A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q–Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q–table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.
Study of JavaScript Static Analysis Tools for Vulnerability Detection in Node.js Packages

With the emergence of the Node.js ecosystem, JavaScript has become a widely-used programming language for implementing server-side web applications. In this paper, we present the first empirical study of static code analysis tools for detecting vulnerabilities in Node.js code. To conduct a comprehensive tool evaluation, we created the largest known curated dataset of Node.js code vulnerabilities. We characterized and annotated a set of 957 vulnerabilities by analyzing information contained in npm advisory reports. We tested nine different tools and found that many important vulnerabilities appearing in the OWASP Top-10 are not detected by any tool. The three best performing tools combined only detect up to 57.6% of all vulnerabilities in the dataset, but at a very low precision of 0.11%. Our curated dataset offers a new benchmark to help characterize existing Node.js code vulnerabilities and foster the development of better vulnerability detection tools for Node.js code.
A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification

The research aims to compare the classification performance of natural disaster messages classification from Twitter. The research experiment covers the analysis of three-word embedding-based extraction feature techniques and five different models of deep learning. The word embedding techniques that are used in this experiment are Word2Vec, fastText, and Glove. The experiment uses five deep learning models, namely three models of different dimensions of Convolutional Neural Network (1D CNN, 2D CNN, 3D CNN), Long Short-Term Memory Network (LSTM), and Bidirectional Encoder Representations for Transformer (BERT). The models are tested on four natural disaster messages datasets: earthquakes, floods, forest fires, and hurricanes. Those models are tested for classification performance
Large-scale comparison and demonstration of continual learning for adaptive data-driven building energy prediction

Data-driven models have been increasingly employed in smart building energy management. To avoid performance degradation over time, data-driven models need to be continually updated to adapt to the changes in building operations. However, several critical issues in the model update process raised wide concerns, especially the concept drift and catastrophic forgetting issues. The concept drift issue happens when the statistical properties of target variable change over time in unforeseen ways. The catastrophic forgetting issue refers to the process that the previously learnt knowledge or patterns may be diluted and eventually lost in model update. Although a few model update methods were proposed, there is a lack of comprehensive comparison of the methods for adaptive data-driven building energy prediction. This paper conducted a comprehensive investigation on the performance of three conventional model update methods and five emerging continual learning methods using 2-year data of 100 buildings extracted from an open-source dataset. The results show that continual learning methods are more effective in ensuring long-term accuracy while cutting down on computation time and data storage expenses. The CV-RMSE of Elastic weight consolidation and Gradient episodic memory decreased by around 14% and 8% on average compared with static model and accumulative learning. The comparison results are valuable to the development of adaptive data-driven building energy prediction models which are more reliable over time and robust against changing operation conditions, thus more practically applicable in smart building energy management.
A computer architecture based on disruptive information technologies for drug management in hospitals

The drug management currently carried out in hospitals is inadequate due to several factors, such as processes carried out manually, the lack of visibility of the hospital supply chain, the lack of standardized identification of medicines, inefficient stock management, an inability to follow the traceability of medicines, and poor data exploitation. Disruptive information technologies could be used to develop and implement a drug management system in hospitals that is innovative in all its phases and allows these problems to be overcome. However, there are no examples in the literature that show how these technologies can be used and combined for efficient drug management in hospitals. To help solve this research gap in the literature, this article proposes a computer architecture for the whole drug management process in hospitals that uses and combines different disruptive computer technologies such as blockchain, radio frequency identification (RFID), quick response code (QR), Internet of Things (IoT), artificial intelligence and big data, for data capture, data storage and data exploitation throughout the whole drug management process, from the moment the drug enters the hospital until it is dispensed and eliminated.
Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing.This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model.This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets.In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months).We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.
Deep-Learning-Based Cryptanalysis of Lightweight Block Ciphers Revisited

With the development of artificial intelligence, deep-learning-based cryptanalysis has been actively studied. There are many cryptanalysis techniques. Among them, cryptanalysis was performed to recover the secret key used for cryptography encryption using known plaintext. In this paper, we propose a cryptanalysis method based on state-of-art deep learning technologies (e.g., residual connections and gated linear units) for lightweight block ciphers (e.g., S-DES, S-AES, and S-SPECK). The number of parameters required for training is significantly reduced by 93.16%, and the average of bit accuracy probability increased by about 5.3% compared with previous the-state-of-art work. In addition, cryptanalysis for S-AES and S-SPECK was possible with up to 12-bit and 6-bit keys, respectively. Through this experiment, we confirmed that the-state-of-art deep-learning-based key recovery techniques for modern cryptography algorithms with the full round and the full key are practically infeasible.
WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

Abstract Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG.
Scene Text Recognition Based on Improved CRNN

Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, the image acquisition process is affected by factors such as occlusion, noise, and obstruction, making scene text recognition tasks more challenging. In this paper, we improve the CRNN model for text recognition, which has relatively low accuracy, poor performance in recognizing irregular text, and only considers obtaining text sequence information from a single aspect, resulting in incomplete information acquisition. Firstly, to address the problems of low text recognition accuracy and poor recognition of irregular text, we add label smoothing to ensure the model’s generalization ability. Then, we introduce the smoothing loss function from speech recognition into the field of text recognition, and add a language model to increase information acquisition channels, ultimately achieving the goal of improving text recognition accuracy. This method was experimentally verified on six public datasets and compared with other advanced methods. The experimental results show that this method performs well in most benchmark tests, and the improved model outperforms the original model in recognition performance.
Future Frame Prediction Network for Human Fall Detection in Surveillance Videos

Video fall detection is one of the most significant challenges in computer vision domain, and it usually involves the recognition of events that do not conform to expected falls. Recently, a majority of unsupervised models are popular to address the issues that call for substantial manual labeled training data in supervised learning. However, almost all existing unsupervised methods usually minimize reconstruction errors, which may lead to insufficient reconstruction errors between fall and nonfall video frames because of the powerful representation ability of the neural network. In this article, we propose a novel efficient fall detection method based on future frame prediction. Specifically, attention U-Net with flexible global aggregation blocks that can achieve better performance is regarded as a frame prediction network, achieving that several video frames predict the next future frame. In the training phase, commonly used appearance constraints on intensity and gradient and motion constraint are combined to further generate higher quality frames. Such constraints promote the performance of the prediction network, which can enlarge the difference between the predicted fall frame and the real fall frame. In the testing phase, the fall score based on the error between the predicted frame and the real frame can be computed to distinguish the fall event. Exhaustive experiments have been conducted on the UR fall dataset, multiple cameras fall dataset (MCFD), and high-quality fall simulation dataset, and the results verify the effectiveness of the proposed method and outperform other existing state-of-the-art methods.
Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine

Air quality has always been one of the most important environmental concerns for the general public and society. Using machine learning algorithms for Air Quality Index (AQI) prediction is helpful for the analysis of future air quality trends from a macro perspective. When conventionally using a single machine learning model to predict air quality, it is challenging to achieve a good prediction outcome under various AQI fluctuation trends. In order to effectively address this problem, a genetic algorithm-based improved extreme learning machine (GA-KELM) prediction method is enhanced. First, a kernel method is introduced to produce the kernel matrix which replaces the output matrix of the hidden layer. To address the issue of the conventional limit learning machine where the number of hidden nodes and the random generation of thresholds and weights lead to the degradation of the network learning ability, a genetic algorithm is then used to optimize the number of hidden nodes and layers of the kernel limit learning machine. The thresholds, the weights, and the root mean square error are used to define the fitness function. Finally, the least squares method is applied to compute the output weights of the model. In order to verify the predictive ability of GA-KELM, based on the collected basic data of long-term air quality forecast at a monitoring point in a city in China, the optimized kernel extreme learning machine is applied to predict air quality ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SO</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NO</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PM</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CO</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PM</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration and AQI), with comparative experiments based on convolutional neural networks, recurrent neural networks and long short-term memory. The results show that the proposed model trains faster and makes more accurate predictions.