Reviewerly/exHarmony huggingface dataset:
Dynamic forecasting model of a hybrid photovoltaic/gravity energy storage system for residential applications

Renewable energy will struggle to grow unless cheaper energy storage technologies become available. Furthermore, the proper deployment of these technologies necessitates the development of energy management systems. These latter are considered the key to achieving sustainable, environmental, and economic development. An energy management system properly manages the energy flow of diverse sources in a small-scale power system, such as a microgrid. Forecast modeling methodologies are employed in this situation to assist the energy management system. The aim of this paper is to provide a physical resource-based dynamic simulator forecast model of a hybrid PV/gravity energy storage connected to the grid and residential load. The proposed model forecasts solar radiation, PV power output, and gravity energy storage state of charge on the horizon of one week. The model employs cloud cover and temperature data imported from the Dark Sky API database. Based on the analysis of the obtained findings, the optimal forecasted operation strategy of the hybrid power system enables the grid operators to meet the residential load during the predicted week. The predicted PV power production was compared to the measured experimental data. The normalized root mean square error (nRMSE) and mean absolute percentage error (MAPE) of the proposed forecast model vary between 10.43%-21.56% and 4.61%-12.34%, respectively. The proposed model could enable grid operators to maintain grid stability. The optimal use of energy storage during peak demand periods, will potentially reduce the use of fossil fuel peaking power plants.
Roles of stable versus nonstable cryptocurrencies in Bitcoin market dynamics

This work assesses the roles of nonstable and stable cryptocurrencies in the dynamics of the Bitcoin market. Formally, we aim to study the return and volatility transmissions between Bitcoin and stablecoins in comparison with Bitcoin/nonstable cryptocurrency pairs. Accordingly, we deduce the optimal portfolio weights based on the hedging strategy. Our results explain which cryptocurrency plays the role of safe haven or hedge and whether the relationship can be described more by the contagion effect versus interdependence based on fundamentals. We observed bidirectional return transmissions between Bitcoin and both nonstable as well as stable cryptocurrencies. The volatility transmission is bidirectional for Bitcoin/Ethereum and Bitcoin/Bitcoin Cash over the short and long terms and unidirectional for Bitcoin/Tether and Bitcoin/TrueUSD over the short term. There are no long-term bidirectional volatility transmissions except for Bitcoin/TrueUSD. The optimal portfolio weights of stable (nonstable) cryptocurrencies are found to be lower (higher) than that of Bitcoin.
Multi-level semantic fusion network for Chinese medical named entity recognition

Medical named entity recognition (MNER) is a fundamental component of understanding the unstructured medical texts in electronic health records, and it has received widespread attention in both academia and industry. However, the previous approaches of MNER do not make full use of hierarchical semantics from morphology to syntactic relationships like word dependency. Furthermore, extracting entities from Chinese medical texts is a more complex task because it usually contains for example homophones or pictophonetic characters. In this paper, we propose a multi-level semantic fusion network for Chinese medical named entity recognition, which fuses semantic information on morphology, character, word and syntactic level. We take radical as morphology semantic, pinyin and character dictionary as character semantic, word dictionary as word semantic, and these semantic features are fused by BiLSTM to get the contextualized representation. Then we use a graph neural network to model word dependency as syntactic semantic to enhance the contextualized representation. The experimental results show the effectiveness of the proposed model on two public datasets and robustness in real-world scenarios.
Scale-free network-based differential evolution to solve function optimization and parameter estimation of photovoltaic models

Some recent research reveals that a topological structure in meta-heuristic algorithms can effectively enhance the interaction of population, and thus improve their performances. Inspired by it, we creatively investigate the effectiveness of using a scale-free network in differential evolution algorithm, and propose a scale-free network-based differential evolution method. The novelties of this paper include a scale-free network-based population structure and a new mutation operator designed to fully utilize the neighborhood information provided by a scale-free structure. The elite individuals and population at the latest generation are both employed to guide a global optimization process. In this manner, the proposed algorithm owns balanced exploration and exploitation capabilities to alleviate the drawbacks of premature convergence. Experimental and statistical analyses are performed on the CEC’17 benchmark function suite and the parameter estimation of photovoltaic models. Results demonstrate its superior effectiveness and efficiency in comparison with its competitive peers.
CFPNet-M: A light-weight encoder-decoder based network for multimodal biomedical image real-time segmentation

-Deep learning techniques are proving instrumental in identifying, classifying, and quantifying patterns in medical images. Segmentation is one of the important applications in medical image analysis. The U-Net has become the predominant deep-learning approach to medical image segmentation tasks. Existing U-Net based models have limitations in several respects, however, including: the requirement for millions of parameters in the U-Net, which consumes considerable computational resources and memory; the lack of global information; and incomplete segmentation in difficult cases. To remove some of those limitations, we built on our previous work and applied two modifications to improve the U-Net model: 1) we designed and added the dilated channel-wise CNN module and 2) we simplified the U-shape network. We then proposed a novel light-weight architecture, the Channel-wise Feature Pyramid Network for Medicine (CFPNet-M). To evaluate our method, we selected five datasets from different imaging modalities: thermography, electron microscopy, endoscopy, dermoscopy, and digital retinal images. We compared its performance with several models having a variety of complexities. We used the Tanimoto similarity instead of the Jaccard index for gray-level image comparisons. The CFPNet-M achieves segmentation results on all five medical datasets that are comparable to existing methods, yet require only 8.8 MB memory, and just 0.65 million parameters, which is about 2% of U-Net. Unlike other deep-learning segmentation methods, this new approach is suitable for real-time application: its inference speed can reach 80 frames per second when implemented on a single RTX 2070Ti GPU with an input image size of 256 × 192 pixels.
LKAU-Net: 3D Large-Kernel Attention-Based U-Net for Automatic MRI Brain Tumor Segmentation

Automatic segmentation of brain tumors from multi-modal magnetic resonance images (MRI) using deep learning methods has emerged in assisting the diagnosis and treatment of brain tumors. However, the diversity in location, shape, and appearance of brain tumors and the dominance of background voxels in MRI images make accurate brain tumor segmentation difficult. In this paper, a novel 3D large-kernel attention-based U-Net (LKAU-Net) is proposed to address these problems for accurate brain tumor segmentation. Advantages of convolution and self-attention are combined in the proposed 3D large-kernel (LK) attention module, including local contextual information, long-range dependence, and channel adaptability simultaneously. The network was trained and evaluated on the multi-modal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The effectiveness of the proposed 3D LK attention module has been proved by both five-fold cross-validation and official online evaluation from the organizers. The results show that our LKAU-Net outperformed state-of-the-art performance in delineating all three tumor sub-regions, with average Dice scores of 79.01%, 91.31%, and 85.75%, as well as 95% Hausdorff distances of 26.27, 4.56, and 5.87 for the enhancing tumor, whole tumor, and tumor core, respectively.
An Attention Based YOLOv5 Network for Small Traffic Sign Recognition

With the development of autonomous driving technology, as a part of the intelligent transportation system, traffic sign recognition (TSR) has attracted more and more attention from intelligent transportation researchers. Most of the existing TSR methods are based on deep learning, but most of those are limited by the contradiction between the reliability and the real-time performance of the model, and it is difficult to be widely used in TSR. In the automatic driving system, the accuracy and real-time requirements of target recognition are strict. In particular, the proportion of traffic signs in the image is small, which will greatly increase the difficulty of recognition. Detection errors and untimely information acquisition can easily lead to traffic accidents. Meanwhile, to meet the needs of network deployment on in-vehicle devices, there are also higher requirements for model size and calculation. Therefore, designing a lightweight neural network with fast detection speed and high recognition accuracy is particularly important for the development of autonomous driving technology. In this paper, we proposed an improved YOLOv5 for traffic sign recognition, and conducted experiments on the traffic sign dataset named TT100K. The results show that our method exhibits excellent performance in TSR, and the overall accuracy has been improved, especially for the small-sized targets recognition. The mAP is 6.68% higher than that before the improvement. Moreover, the proposed network can meet the requirements of recognition speed on vehicle-mounted equipment, and the recognition speed can reach 91 FPS, which has a great application value.
Real Time Power Equipment Meter Recognition Based on Deep Learning

Reading power equipment meters often requires loads of manpower, which is a trivial, repetitive and error-prone task. While conventional automated recognition methods using Computer Vision (CV) techniques are inflexible under diverse scenarios. In this paper we propose a lightweight meter recognition method that combines deep learning and traditional CV techniques for automated meter reading. For meter detection, an adaptive anchor and Global Context (GC) module are deployed to improve the feature extraction ability of lightweight backbone without increasing computational cost. Then a FPN and a PANet are developed to realize the information interaction between different feature layers and achieve multiscale prediction. Our method also includes a multi-task segmented network to read the detected meters, accelerating the detection speed. Experiments demonstrate that our proposed method can achieve a detection speed of 123 FPS in GeForce GTX 1080 and can obtain an accuracy of 88.2% mAP50:95. In the case of insufficient training samples, the method can still achieve an accuracy of 80.9% mAP50:95. In addition, we build a Power Meter Images (PMI) dataset which contains 1800 images in real scene. The dataset and method we proposed can help with further upgrades of traditional substations. In the future, we also hope to extend the algorithm to edge computing cameras for substations. The newly developed dataset and code are available at https://github.com/zzfan3/electric&#x005F;meter&#x005F;detect&#x005F;recognize.
An Adaptive Self-modeling Network Model for Multilevel Organizational Learning

Multilevel organizational learning concerns an interplay of different types of learning at individual, team, and organizational levels. These processes use complex dynamic and adaptive mechanisms. A second-order adaptive network model for this is introduced here and illustrated.
RT-TEE: Real-time System Availability for Cyber-physical Systems using ARM TrustZone

Embedded devices are becoming increasingly pervasive in safety-critical systems of the emerging cyber-physical world. While trusted execution environments (TEEs), such as ARM TrustZone, have been widely deployed in mobile platforms, little attention has been given to deployment on real-time cyber-physical systems, which present a different set of challenges compared to mobile applications. For safety-critical cyber-physical systems, such as autonomous drones or automobiles, the current TEE deployment paradigm, which focuses only on confidentiality and integrity, is insufficient. Computation in these systems also needs to be completed in a timely manner (e.g., before the car hits a pedestrian), putting a much stronger emphasis on availability.To bridge this gap, we present RT-TEE, a real-time trusted execution environment. There are three key research challenges. First, RT-TEE bootstraps the ability to ensure availability using a minimal set of hardware primitives on commodity embedded platforms. Second, to balance real-time performance and scheduler complexity, we designed a policy-based event-driven hierarchical scheduler. Third, to mitigate the risks of having device drivers in the secure environment, we designed an I/O reference monitor that leverages software sandboxing and driver debloating to provide fine-grained access control on peripherals while minimizing the trusted computing base (TCB).We implemented prototypes on both ARMv8-A and ARMv8-M platforms. The system is tested on both synthetic tasks and real-life CPS applications. We evaluated rover and plane in simulation and quadcopter both in simulation and with a real drone.
Real-Time ISR-YOLOv4 Based Small Object Detection for Safe Shop Floor in Smart Factories

Wearing a hard hat can effectively improve the safety of workers on a construction site. However, workers often take off their helmets because they have a weak sense of safety and are uncomfortable, and this action poses a large danger. Workers not wearing hard hats are more likely to be injured in accidents such as human falls and vertical falls. Therefore, the detection of wearing a helmet is an important step in the safety management of a construction site, and it is urgent to detect helmets quickly and accurately. However, the existing manual monitor is labor intensive, and it is difficult to popularize the method of mounting the sensor on the helmet. Thus, in this paper, we propose an AI method to detect the wearing of a helmet with satisfactory accuracy with a high detection rate. Our method selects based on YOLOv4 and adds an image super resolution (ISR) module at the end of the input. Afterward, the image resolution is increased, and the noise in the image is removed. Then, dense blocks are used to replace residual blocks in the backbone network using the CSPDarknet53 framework to reduce unnecessary computation and reduce the number of network structure parameters. The neck then uses a combination of SPPnet and PANnet to take full advantage of the small target’s capabilities in the image. We add foreground and background balance loss functions to the YOLOv4 loss function part to solve the image background and foreground imbalance problem. Experiments performed using self-constructed datasets show that the proposed method has more efficacy than the currently available small target detection methods. Finally, our model achieves an average precision of 93.3%, a 7.8% increase over the original algorithm, and it takes only 3.0 ms to detect an image at 416 × 416.
The infinite race between steganography and steganalysis in images

• Over 150 significant works in Steganography and Steganalysis surveyed • Taxonomies, gaps in the domains and key features of each paper were highlighted • Many studies in Steganography and Steganalysis utilize only a single dataset • Many Steganalysis methods seem to target only specific steganography schemes • Coverless and GAN-based steganography are not adequately countered • More studies should deal with Cover Source Mismatch and robustness to retouching Steganography is the primary method by which individuals can communicate covertly; cryptography, on the other hand, fails at this, as it is possible to detect (the presence of) encrypted-communication. Steganalysis has been used to detect the presence of steganography and acts as a countermeasure to it. The ongoing race between image-steganography and steganalysis methods has resulted in the need for this paper which surveys and compares developments in these two intertwined-fields. This work covers over 150 papers that demonstrate the significant improvements made in steganography and steganalysis over the last three-decades. We mention the novelty of the method proposed in each paper, as well as the evaluation results and the paper's contribution to the field. We provide several taxonomies for steganography and steganalysis methods, based on the approach and techniques underlying the methods, which allows us to perform the first comprehensive comparison of steganography and steganalysis methods. This comparison sheds light on the existing-gaps between the two connected domains and can be used to identify and prioritize the steganography methods that require immediate remediation using steganalysis methods. Lastly, we follow the chronological-evolution of steganography and steganalysis methods over the years, an overview which highlights the infinite-nature of this race.
Parallel fuzzy relation matrix factorization towards algebraic formulation, universal approximation and interpretability of MIMO hierarchical fuzzy systems

Multi-input multi-output (MIMO) hierarchical fuzzy systems (HFSs) play a significant role in industrial process control and power systems. Using the max-min semi-tensor product of matrices, this paper proposes the algebraic formulation of MIMO HFSs, and discusses the corresponding interpretability. Firstly, the parallel fuzzy relation matrix (FRM) of MIMO fuzzy systems is proposed, based on which, an algebraic formulation of MIMO fuzzy systems is developed. Secondly, the factorization of parallel FRM is used to partition the MIMO fuzzy systems into fuzzy logic units (FLUs). Based on this operation, the algebraic formulations and construction algorithms of serial, parallel and hybrid MIMO HFSs are established. Thirdly, the algebraic formulations are used to study the universal approximation and interpretability of MIMO HFSs, and the optimal interpretability is obtained. Finally, the effectiveness of the obtained results is verified by the energy control systems in parallel hybrid vehicles.
Incentive Mechanisms in Federated Learning and A Game-Theoretical Approach

Federated learning (FL) represents a new machine learning paradigm, utilizing various resources from participants to collaboratively train a global model without exposing the privacy of training data. The learning performance critically depends on various resources provided by participants and their active participation. Hence, it is essential to enable more participants to actively contribute their valuable resources in FL. In this article, we present a survey of incentive mechanisms for FL. We identify the incentive problem, outline its framework, and categorically discuss the state-of-the-art incentive mechanisms in Shapley value, Stackelberg game, auction, contract, and reinforcement learning. In addition, we propose three multi-dimensional game-theoretical models to study the economical behaviors of participants and demonstrate their applicability in cross-silo FL scenarios.
Energy-Efficient Resource Allocation for Federated Learning in NOMA-Enabled and Relay-Assisted Internet of Things Networks

Distributed machine learning (ML) algorithms are imperative for the next-generation Internet of Things (IoT) networks, thanks to preserving the privacy of users’ data and efficient usage of the communication resources. Federated learning (FL) is a promising distributed ML algorithm where the models are trained at the edge devices over the local data sets, and only the model parameters are shared with the cloud server (CS) to generate global model parameters. Nevertheless, due to the limited battery life of the edge devices, improving the energy-efficiency is a prime concern for FL. In this work, we investigate a resource allocation scheme to reduce the overall energy consumption of FL in the relay-assisted IoT networks. We aim at minimizing the overall energy consumption of IoT devices subject to the FL time constraint. FL time consists of model training computation time and wireless transmission latency. Toward this goal, a joint optimization problem, considering scheduling the IoT devices with the relays, transmit power allocation, and computation frequency allocation, is formulated. Due to the NP-hardness of the joint optimization problem, a global optimal solution is intractable. Therefore, leveraging graph theory, joint near-optimal, and low-complexity suboptimal solutions are proposed. Efficiency of our proposed solutions over several benchmark schemes is verified via extensive simulations. Simulation results show that the proposed near-optimal scheme achieves 6, 4, and 2 times lower energy consumption, respectively, compared to the considered fixed, computation adaptation, and power adaptation schemes. Such an appealing energy efficiency comes at the cost of slightly increased FL time compared to the fixed and computation only adaptation schemes.
A survey of historical document image datasets

Abstract This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.