Reviewerly/exHarmony huggingface dataset:
EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records

Abstract Privacy concerns often arise as the key bottleneck for the sharing of data between consumers and data holders, particularly for sensitive data such as Electronic Health Records (EHR). This impedes the application of data analytics and ML-based innovations with tremendous potential. One promising approach for such privacy concerns is to instead use synthetic data. We propose a generative modeling framework, EHR-Safe, for generating highly realistic and privacy-preserving synthetic EHR data. EHR-Safe is based on a two-stage model that consists of sequential encoder-decoder networks and generative adversarial networks. Our innovations focus on the key challenging aspects of real-world EHR data: heterogeneity, sparsity, coexistence of numerical and categorical features with distinct characteristics, and time-varying features with highly-varying sequence lengths. Under numerous evaluations, we demonstrate that the fidelity of EHR-Safe is almost-identical with real data (&lt;3% accuracy difference for the models trained on them) while yielding almost-ideal performance in practical privacy metrics.
A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction

Water quality prediction plays a crucial role in pollution treatment. However, inaccurate long-term prediction resulting from complex information patterns and insufficient feature extraction may lead to unnecessary environmental costs. In this study, a novel Interval Decomposition Correlation Particle Swarm Optimization-Extreme Learning Machine (IDCPSO-ELM) model is proposed to improve long-term water quality prediction ability. We employ Multivariate Variational Mode Decomposition (MVMD), Variational Mode Decomposition (VMD), Sliding Correlation and Permutation Entropy to reconstruct features to reduce complexity. Seasonal and Trend decomposition using Loess-Empirical Wavelet Transform decomposition (STL-EWT) is applied to target variables to improve feature extraction ability. Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) is used to predict high-frequency components based on the reconstructive features, and Back Propagation Neural Network-Extreme Learning Machine (BPNN-ELM) is employed to predict the trend and lowest-frequency components. Grey Wolf Optimization Algorithm (GWO) is used to combine these results. Six stations with high potential pollution threats in the Haihe River basin of Beijing from 2021 to 2022 are taken into model competition. The results show that: (1) In short-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.0934, 0.0410 and 0.8061, respectively, which are better than Genetic Algorithm-Elman Network (GA-ELMAN), Cuckoo Search Algorithm-Back Propagation Neural Network (CS-BPNN), PSO-ELM and Sparrow Search Algorithm-Kernel Extreme Learning Machine (SSA-KELM) short-term prediction models. (2) In long-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.1114, 0.0420 and 0.8268, respectively, which are better than the Radial Basis Function Neural Network (RBFNN), Grey Wolf Optimization-Support Vector Machine Regression (GWO-SVR), Whale Optimization Algorithm-Deep Extreme Learning Machine (WOA-DELM), Wavelet Transform decomposition-GWO-SVR-BPNN (WT-GWO-SVR-BPNN) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-PSO-ELM-Long Short-Term Memory (CEEMDAN-PSO-ELM-LSTM) long-term prediction models. IDCPSO-ELM model is considered competitive and promising for long-term water quality prediction, particularly in areas with high potential pollution threats.
Breast cancer classification using deep learned features boosted with handcrafted features

Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art.
Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on &lt;i&gt;k&lt;/i&gt;-Means and &lt;i&gt;k&lt;/i&gt;-Nearest Neighbors Algorithms

Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %.
Homomorphic federated learning schemes enabled pedestrian and vehicle detection system

Intelligent transport systems are increasingly being used in practice these days. Fog nodes and cloud servers collect real-time pedestrian and vehicle data and train them based on machine learning models. Existing pedestrian and vehicle detection systems need more security, less resource leakage, and faster processing. This paper proposes a homomorphic, secure, federated learning-enabled pedestrian detection system named HMFLS. The HMFLS consists of base stations (BS) and homogeneous (homo) federated learning servers with weights, surveillance, and traffic light components, and these nodes serve as data generation and processing sources. Homomorphic encryption is a cryptographic technique that allows computations on encrypted data without decryption. In other words, it enables computations on encrypted data while preserving the privacy and confidentiality of the information. The HMFLS exploits Generative Adversarial Networks to train pedestrian and vehicle images and extract features based on VGG19 from fog nodes and surveillance sensor images. We trained the different tasks and weights based on the model’s 28,000 pedestrian and vehicle images. The goal is to identify pedestrians and vehicles in the system. The interface is based on an Android-based application that can be easily integrated into different vehicles and mobile phones. Simulation results demonstrated that HMFL performed well as compared to existing schemes (e.g., PEL, TFL-CNN, FLAV) in terms of security accuracy by 98%, resource leakage by 50%, and processing time by 52% for vehicle and pedestrian detection in the system.
Boundary-aware local Density-based outlier detection

Outlier detection is crucial for improving the performance of machine learning algorithms and is particularly vital in data sets possessing a small number of points. While the existing outlier detection methods deliver good results on a certain data set, the results are rather down on some data sets. Besides all these aspects, there is also a need for an algorithm that quickly processes high-dimensional data sets. To satisfy these requirements, we propose an unsupervised local outlier detection method that can draw the neighborhood boundaries of the data points via Chebyshev inequality. The proposed method sets the boundaries of the points through the so-called deviation parameter that correlates to the standard deviation of the data distribution and then detects outliers by quantifying their neighborhood densities. The experimental results on real-world and synthetic data sets show the efficacy of the proposed method in comparison to the state-of-the-art methods. The source code of the proposed algorithm and the data sets are at https://github.com/fatihaydin1/BLDOD.
Solar Irradiance Anticipative Transformer

This paper proposes an anticipative transformer-based model for short-term solar irradiance forecasting. Given a sequence of sky images, our proposed vision transformer encodes features of consecutive images, feeding into a transformer decoder to predict irradiance values associated with future unseen sky images. We show that our model effectively learns to attend only to relevant features in images in order to forecast irradiance. Moreover, the proposed anticipative transformer captures long-range dependencies between sky images to achieve a forecasting skill of 21.45 % on a 15 minute ahead prediction for a newly introduced dataset of all-sky images when compared to a smart persistence model.
Bicubic++: Slim, Slimmer, Slimmest Designing an Industry-Grade Super-Resolution Network

We propose a real-time and lightweight single-image super-resolution (SR) network named Bicubic++. Despite using spatial dimensions of the input image across the whole network, Bicubic++ first learns quick reversible downgraded and lower resolution features of the image in order to decrease the number of computations. We also construct a training pipeline, where we apply an end-to-end global structured pruning of convolutional layers without using metrics like magnitude and gradient norms, and focus on optimizing the pruned network’s PSNR on the validation set. Furthermore, we have experimentally shown that the bias terms take considerable amount of the runtime while increasing PSNR marginally, hence we have also applied bias removal to the convolutional layers. Our method adds ~1dB on Bicubic upscaling PSNR for all tested SR datasets and runs with ~1.17ms on RTX3090 and ~2.9ms on RTX3070, for 720p inputs and 4K outputs, both in FP16 precision. Bicubic++ won NTIRE 2023 RTSR Track 2 × 3 SR competition and is the fastest among all competitive methods. Being almost as fast as the standard Bicubic upsampling method, we believe that Bicubic++ can set a new industry standard.
Contrastive Learning for Depth Prediction

Depth prediction is at the core of several computer vision applications, such as autonomous driving and robotics. It is often formulated as a regression task in which depth values are estimated through network layers. Unfortunately, the distribution of values on depth maps is seldom explored. Therefore, this paper proposes a novel framework combining contrastive learning and depth prediction, allowing us to pay more attention to depth distribution and consequently enabling improvements to the overall estimation process. Purposely, we propose a window-based contrastive learning module, which partitions the feature maps into non-overlapping windows and constructs contrastive loss within each one. Forming and sorting positive and negative pairs, then enlarging the gap between the two in the representation space, constraints depth distribution to fit the feature of the depth map. Experiments on KITTI and NYU datasets demonstrate the effectiveness of our framework.
Large Kernel Distillation Network for Efficient Single Image Super-Resolution

Efficient and lightweight single-image super-resolution (SISR) has achieved remarkable performance in recent years. One effective approach is the use of large kernel designs, which have been shown to improve the performance of SISR models while reducing their computational requirements. However, current state-of-the-art (SOTA) models still face problems such as high computational costs. To address these issues, we propose the Large Kernel Distillation Network (LKDN) in this paper. Our approach simplifies the model structure and introduces more efficient attention modules to reduce computational costs while also improving performance. Specifically, we employ the reparameterization technique to enhance model performance without adding extra cost. We also introduce a new optimizer from other tasks to SISR, which improves training speed and performance. Our experimental results demonstrate that LKDN outperforms existing lightweight SR methods and achieves SOTA performance.
Rethinking Dilated Convolution for Real-time Semantic Segmentation

The field-of-view is an important metric when designing a model for semantic segmentation. To obtain a large field-of-view, previous approaches generally choose to rapidly downsample the resolution, usually with average poolings or stride 2 convolutions. We take a different approach by using dilated convolutions with large dilation rates throughout the backbone, allowing the backbone to easily tune its field-of-view by adjusting its dilation rates, and show that it’s competitive with existing approaches. To effectively use the dilated convolution, we show a simple upper bound on the dilation rate in order to not leave gaps in between the covolutional weights, and design an SEResNeXt inspired block structure that uses two parallel 3 × 3 convolutions with different dilation rates to preserve the local details. Manually tuning the dilation rates for every block can be difficult, so we also introduce a differentiable neural architecture search method that uses gradient descent to optimize the dilation rates. In addition, we propose a lightweight decoder that restores local information better than common alternatives. To demonstrate the effectiveness of our approach, our model RegSeg achieves competitive results on real-time Cityscapes and CamVid datasets. Using a T4 GPU with mixed precision, RegSeg achieves 78.3 mIOU on Cityscapes test set at 37 FPS, and 80.9 mIOU on CamVid test set at 112 FPS, both without ImageNet pretraining.
Multi-Task Learning based Video Anomaly Detection with Attention

Multi-task learning based video anomaly detection methods combine multiple proxy tasks in different branches to detect video anomalies in different situations. Most existing methods suffer from one of these shortcomings: I) Combination of proxy tasks in their methods is not in a complementary and explainable way. II) Class of the object is not effectively considered. III) All motion anomaly cases are not covered. IV) Context information is not engaged in anomaly detection. To address these shortcomings, we propose a novel multi-task learning based method that combines complementary proxy tasks to better consider the motion and appearance features. In one branch, motivated by the abilities of the semantic segmentation and future frame prediction tasks, we combine them into a novel task of future semantic segmentation prediction to learn normal object classes and consistent motion patterns, and to detect respective anomalies simultaneously. In the second branch, we leverage optical flow magnitude estimation for motion anomaly detection and we propose an attention mechanism to engage context information in normal motion modeling and to detect motion anomalies with attention to object parts, the direction of motion, and the distance of the objects from the camera. Our qualitative results show that the proposed method considers the object class effectively and learns motion with attention to the aforementioned determinant factors which results in precise motion modeling and better motion anomaly detection. Additionally, quantitative results show the superiority of our method compared with state-of-the-art methods.
Reparameterized Residual Feature Network For Lightweight Image Super-Resolution

In order to solve the problem of deploying super-resolution technology on resource-limited devices, this paper explores the differences in performance and efficiency between information distillation mechanism and residual learning mechanism used in lightweight super-resolution, and proposes a lightweight super-resolution network structure based on reparameterization, named RepRFN, which can effectively reduce GPU memory consumption and improve inference speed. A multi-scale feature fusion structure is designed so that the network can learn and integrate features of various scales and high-frequency edges. We rethought the redundancy existing in the overall network framework, and removed some redundant modules without affecting the overall performance as much as possible to further reduce the complexity of the model. In addition, we introduced a loss function based on Fourier transform to transform the spatial domain of the image into the frequency domain, so that the network can supervise and learn the frequency part of the image. The experimental results show that the RepRFN designed in this paper achieves relatively low complexity while ensuring certain performance, which is conducive to the deployment of Edge devices. Code is available at https://github.com/laonafahaodange/RepRFN.
Mixer-based Local Residual Network for Lightweight Image Super-resolution

Recently, the single image super-resolution (SISR) based on deep learning algorithm has taken more attention from the research community. There are many methods that are developed to solve this task using CNNs methods. However, most of these methods need large computational resources and consume more runtime. Due to the fact that the runtime is essential for some applications, we propose a mixer-based local residual network (MLRN) for lightweight image super-resolution (SR). The idea of the MLRN model is based on mixing channel and spatial features and mixing low and high-frequency information. This is done by designing a mixer local residual block (MLRB) to be the backbone of our model. Moreover, the bilinear up-sampling is utilized to transfer and mix low-frequency information with extracted high-frequency information. Finally, the GELU activation is used in the main model, proving its efficiency for the SR task. The experimental results show the effectiveness of the model against other state-of-the-art lightweight models. Finally, we took part in the Efficient Super-Resolution 2023 Challenge and achieved good results.
Blockchain-Based Microgrid for Safe and Reliable Power Generation and Distribution: A Case Study of Saudi Arabia

Energy demand is increasing rapidly due to rapid growth and industrialization. It is becoming more and more complex to manage generation and distribution due to the diversification of energy sources to minimize carbon emissions. Smart grids manage reliable power generation and distribution efficiently and cater to a large geographical area and population, but their centralized structure makes them vulnerable. Cybersecurity threats have become a significant concern with these systems’ increasing complexity and connectivity. Further transmission losses and its vulnerability to the single point of failure (SPOF) are also major concerns. Microgrids are becoming an alternative to large, centralized smart grids that can be managed locally with fewer user bases and are safe from SPOF. Microgrids cater to small geographical areas and populations that can be easily managed at the local level and utilized for different sources of energy, like renewable energy. A small group of consumers and producers are involved, but microgrids can also be connected with smart grids if required to exchange the excess energy. Still, these are also vulnerable to cybersecurity threats, as in the case of smart grids, and lack trust due to their decentralized nature without any trusted third party. Blockchain (BC) technology can address the trust and cybersecurity challenges in the energy sector. This article proposes a framework for implementing a BC-based microgrid system for managing all the aspects of a microgrid system, including peer-to-peer (P2P) energy trading, Renewable Energy Certificate (REC), and decentralized energy trading, that can be utilized in the case of Saudi Arabia. It can integrate cybersecurity standards and protocols, as well as the utilization of smart contracts, for more secure and reliable energy generation and distribution with transparency.
An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field

Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved.