Reviewerly/exHarmony huggingface dataset:
A fusion kernel in SVM and improved evolutionary algorithm in feature selection for Parkinson's disease detection

Support Vector Machine (SVM) is an excellent Machine Learning (ML) method and Kernel Function (KF) in SVM can effectively accelerate the calculation and affect the results in ML. Therefore, the selection of KF has become a research hotspot. In small-sample medical detection learning tasks, there are often imbalanced data categories and data overlaps. To alleviate these situations, we adopt feature selection in the data preprocessing stage and optimize the support vector machine learner with fusion kernel function. In this paper, SVM based on Fusion KF(FKF-SVM) is used for Parkinson's Disease (PD) detection to improve the performance of traditional SVM. The improved KF used in the paper includes Analysis of Variance (ANOVA) kernel, triangular kernel, log kernel and wavelet kernel. The proposed KF is evaluated by using two popular and publicly available data sets from University of California Irvine (UCI) repository with different parameters like accuracy, recall, F1score, Area Under the Curve (AUC) in classification and Mean Square Error (MSE), Mean Absolute Error (MAE), R2score, Explained Variance Score (EVS) in regression. According to the experimental results, the triangular kernel is the winner in the classification that achieves an accuracy of 93%, recall of 96%, F1score of 96% and AUC of 97%, which is increased by ten percentage points at least compared to the traditional KF. In the regression, the ANOVA kernel is a superior KF which obtains an MAE of 2.3, R2score of 0.81 that is 200% lower than the traditional KF at least.
FedDAD: Solving the Islanding Problem of SAR Image Aircraft Detection Data

In aircraft feature detection, the difficulty of acquiring Synthetic Aperture Radar (SAR) images leads to the scarcity of some types of aircraft samples, and the high privacy makes the personal sample set have the characteristics of data silos. Existing data enhancement methods can alleviate the problem of data scarcity through feature reuse, but they are still powerless for data that are not involved in local training. To solve this problem, a new federated learning framework was proposed to solve the problem of data scarcity and data silos through multi-client joint training and model aggregation. The commonly used federal average algorithm is not effective for aircraft detection with unbalanced samples, so a federal distribution average deviation (FedDAD) algorithm, which is more suitable for aircraft detection in SAR images, was designed. Based on label distribution and client model quality, the contribution ratio of each client parameter is adaptively adjusted to optimize the global model. Client models trained through federated cooperation have an advantage in detecting aircraft with unknown scenarios or attitudes while remaining sensitive to local datasets. Based on the YOLOv5s algorithm, the feasibility of federated learning was verified on SAR image aircraft detection datasets and the portability of the FedDAD algorithm on public datasets. In tests based on the YOLOv5s algorithm, FedDAD outperformed FedAvg’s mAP0.5–0.95 on the total test set of two SAR image aircraft detection and far outperformed the local centralized training model.
Albedo measurements and energy yield estimation uncertainty for bifacial photovoltaic systems

Abstract The deployment of bifacial photovoltaic (PV) modules for utility‐scale PV plants has quickly proliferated in recent years. As they can harness the irradiance received at the rear side of bifacial generators—which depends on the albedo of the reflector ground—bifacial modules provide higher productivity than monofacial modules, in exchange for a low increase in manufacturing costs. However, the reduction of PV performance estimations uncertainty still represents a main concern for both PV and financial actors, which seek to reduce financial risks of bifacial PV systems. The aim of this work is to provide a better understanding of the impact that albedo measurements have on PV yield estimations uncertainty. We analysed the albedo sensitivity coefficient relating albedo measurement and subsequent energy yield estimations' uncertainties through a representative yield simulation exercise performed for three different sites (Chile, Spain and Sweden) and two types of PV plants: static and single‐axis tracker. A formula considering the impact of Ground Coverage Ratio and Inverter Loading Ratio is proposed for this coefficient. We performed an experimental campaign to evaluate the uncertainty associated to different albedo measurement conditions. Surface reflectance of five different ground surfaces has been measured using four sensors, which allowed to obtain three useful types of albedos that we defined and described. A series of recommendations for short‐term ground‐based albedo measurements that entail a maximum of 1.5% yield uncertainty contribution in the frame of commercial PV plants are then derived. A representative case of actual albedo measurement practices at the current PV market scene is also presented in the form of a commercial albedo measurement project for a future 50 MW bifacial PV plant. Despite being a local characteristic, the most relevant uncertainty source found is associated to the non‐homogeneity of the future PV plant's surface. An important consideration is that changes in the use of land caused by the construction of a PV plant lead to an albedo uncertainty that predominates over all the uncertainty sources associated to the albedo measurement conditions. This can also be the case for long‐term seasonal variations, which can become the dominant uncertainty source for high latitude locations.
A Literature Review and Critical Analysis of Metaheuristics Recently Developed

Abstract Metaheuristic algorithms have applicability in various fields where it is necessary to solve optimization problems. It has been a common practice in this field for several years to propose new algorithms that take inspiration from various natural and physical processes. The exponential increase of new algorithms is a controversial issue that several researchers have criticized. However, their efforts to point out multiple issues involved in these practices have been insufficient since the number of existing metaheuristics continues to increase yearly. To know the current state of this problem, this paper analyzes a sample of 111 recent studies where so-called new, hybrid, or improved optimization algorithms are proposed. Throughout the document, the topics reviewed will be addressed from a general perspective to their specific aspects. Among the study’s findings, it is observed that only 43% of the analyzed papers make some mention of the No Free Lunch (NFL) theorem, being this significant result ignored by most of the studies where new algorithms are presented. Of the analyzed studies, 65% present an improved version of some established algorithm, which reveals that the trend is no longer to propose metaheuristics based on new analogies. Additionally, a compilation of solutions found in engineering problems commonly used to verify the performance of state-of-the-art algorithms is presented. To demonstrate that algorithms with a low level of innovation can be erroneously considered as new frameworks for years, the metaheuristics known as Black Widow Optimization and Coral Reef Optimization are analyzed. The study of its components reveals that they do not have any innovation. Instead, they are just deficient mixtures of different evolutionary operators. This result applies by extension to their recently proposed improved versions.
A novel robust iterated CKF for GNSS/SINS integrated navigation applications

Abstract In challenging circumstances, the estimation performance of integrated navigation parameters for tightly coupled GNSS/SINS is impacted by outlier measurements. An effective solution that employs a novel iterative sigma-point structure with a modified robustness optimization approach for enhancing the error compensation effectiveness and robustness of filters utilized in GNSS challenge conditions is proposed in this paper. The proposed method modifies the CKF scheme by incorporating nonlinear regression and numerous iteration processes for ameliorating error compensation. Subsequently, a loss function and penalty mechanism are implemented to enhance the filter's robustness to outlier measurements. Furthermore, to fully incorporate valid information of the innovation and speed up the operation of the proposed method, the outlier measurement detection criteria are established to bypass the penalty mechanism against measurement weights in the absence of outliers in GNSS measurements. Field experiments demonstrate that the proposed method outperforms traditional methods in mitigating navigation errors, particularly when multipath errors and non-line-of-sight (NLOS) reception are increased.
Microseismic network sensitivity in case of no seismic activity

Abstract Underground human activities, such as mining, shale gas, and oil exploitation, waste-water disposal, or geothermal plants, can cause earthquakes; therefore, they are monitored by local seismic networks. An ideal seismic network has a triangulated grid, with spacing equal twice the minimal depth and no associated industry noise. In real cases, the network sensitivity is biased by stations placed near noisy roads, factories, or in a private garden, none located at optimal nodes. The sensitivity is also a function of the detection algorithm type and setting. The goal of this case study is to suggest a work-flow for network sensitivity calculation in case of no seismic activity. In other words: how small are the earthquakes that such seismic networks would detect if they were present? Such network sensitivity is a function of stations noise level, station-source geometry, and setting of the detection algorithm. A brief theory and work-flow description is followed by two real-case demonstrations from Czech Republic, Europe: first, a proof-test on a well-studied seismically active area of West Bohemia/Vogtland and second, an application to an uprising geothermal project in Litoměřice, where no seismic activity was detected in years of monitoring.
Global priors guided modulation network for joint super-resolution and SDRTV-to-HDRTV

Watching low resolution standard dynamic range (LR SDR) video on a 4K high dynamic range (HDR) TV is not the best viewing experience. Joint super-resolution (SR) and SDRTV-to-HDRTV aims to enhance the visual quality of LR SDR videos that have quality deficiencies in resolution and dynamic range. Previous methods that rely on learning local information typically cannot do well in preserving color conformity and long-range structural similarity, resulting in unnatural color transition and texture artifacts. In order to tackle these challenges, we propose a global priors guided modulation network (GPGMNet). In particular, we design a global priors extraction module (GPEM) to extract color conformity prior and structural similarity prior that are beneficial for SDRTV-to-HDRTV and SR tasks, respectively. To further exploit the global priors and preserve spatial information, we devise multiple global priors-guided spatial-wise modulation blocks (GSMBs) with a few parameters for intermediate feature modulation. In these GSMBs, the modulation parameters are generated by the shared global priors and the spatial features map from the spatial pyramid convolution block (SPCB). With these elaborate designs, the GPGMNet can achieve higher visual quality with lower computational complexity. Extensive experiments demonstrate that our proposed GPGMNet is superior to the state-of-the-art methods. Specifically, our proposed model exceeds the state-of-the-art by 0.64 dB in PSNR, with 69% fewer parameters and 3.1× speedup.
Knowledge-driven adaptive evolutionary multi-objective scheduling algorithm for cloud workflows

Workflow scheduling in cloud platforms is a highly challenging issue because it faces multiple conflicting optimization objectives and large-scale decision variables. Most of the existing multi-objective workflow scheduling algorithms regard the focused problems as black boxes, and optimize large-scale decision variables as a whole. This leads to inefficiency in searching solution spaces that grow exponentially with the increase of decision variables. To compensate the above deficiency, this paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm, KAMSA for short, to optimize makespan and cost of workflow execution in cloud platforms. Specifically, we excavate the knowledge that adjustment of a task's execution only affects its successor tasks to divide large-scale decision variables into a series of groups, so as to give play to the strengths of divide-and-conquer technology to improve the evolutionary search efficiency. Moreover, we develop an adaptive resource allocation scheme to reward more evolution opportunities for groups with high contributions to further improve the evolutionary search efficiency. We compare the proposed KAMSA with five state-of-the-art competitors in the context of 20 real-world workflows and the Amazon elastic compute cloud (EC2). The comparison results verify the KAMSA's advantages by prevailing over the five competitors on 18 out of the 20 test cases with respect to the metric hypervolume.
A blockchain-based privacy-preserving auditable authentication scheme with hierarchical access control for mobile cloud computing

Blockchain-based authentication mode, a fundamental solution to prevent unauthorized access behavior, gradually becomes a focus in future distributed mobile cloud computing (MCC) services. However, due to the transparent and immutable characteristics of blockchain, users' access behaviors are facing huge security and privacy threats. Storing the encrypted data on chain is an effective way to address these issues, but access permission confirmation and update in the form of ciphertext is the main bottleneck. To this end, this paper proposes a blockchain-based unified authentication and hierarchical access control scheme for the MCC environment, which provides both privacy protection and auditability. In the proposed scheme, users can access multiple MCC services with different access permissions using a single credential. To protect the privacy of both users and service providers, while still supporting auditability, the data on the public ledger is blinded using Pedersen commitments. Besides, the proposed scheme provides flexible dynamic updating in encrypted form. Theoretical analysis indicates that the proposed scheme can meet various security and privacy requirements in the MCC environment. Compared with related schemes, it has better communication efficiency. Therefore, the proposed scheme is more suitable for the actual MCC environment.
Efficient one-off clustering for personalized federated learning

In traditional federated learning such as FedAvg, the associations among clients are often ignored when executing on non-independent or heterogeneously distributed datasets, resulting in unsatisfactory accuracy. Although some previous works on clustered federated learning have been proposed to address such problems, most of them have a polarized problem. When the number of clustering is small, the model performs poorly and fails to accurately capture the distinction between clients. While a large number of clustering times tends to lead to higher communication costs. Therefore, a critical need is to design an efficient clustered federated solution that can both better capture the diversity between local clients and minimize the communication and computation costs. To this end, we propose an efficient one-off clustered federated learning framework called FedEOC. FedEOC exploits the "learning-to-learn" characteristic of meta-learning to enhance the generalization of the model across different clients so that only a small number of iterations are needed for each client to quickly obtain locally adapted weights. Based on the well-initially trained weights on all clients, we can cluster the clients only once to achieve the effect of one-off clustering and multiple-round applying. Additionally, to alleviate the issue of cluster imbalance, FedEOC is equipped with a Decomposition and Consolidation (Dec-Con) mechanism to decompose the clients from the extreme clusters and consolidate them into the most similar ones. The comprehensive experiments conducted on two real-world datasets demonstrate the superior capability of FedEOC from both aspects of accuracy and efficiency.
Smart contract-based integrity audit method for IoT

With the widespread adoption of Internet of Things (IoT) technology, a large amount of private data has been generated. Due to the limited computing and storage resources of IoT devices, enterprises and individuals choose to use a cloud storage provider (CSP) to manage their data. However, they lose the actual control of the data, and hence users need to verify the integrity of the data in the cloud. As a result, reducing the computational overhead of integrity auditing tasks for massive amounts of data and optimizing the distribution of benefits among the participants have become the key to the healthy development of IoT technologies. This paper proposes an integrity auditing method based on smart contracts. First, we construct a noninteractive auditing model based on a chaotic system so that the third-party auditor (TPA) can complete the auditing task without challenging the CSP. Second, we proposed a new construction method for audit trees that can quickly generate the verification tag of the root node. Finally, we design a smart contract-based auditing algorithm on the blockchain, which can simultaneously verify the behaviour of the CSP and the TPA to achieve fair payments. The theoretical analysis shows that our scheme can effectively prevent the TPA from falsifying audit results and prevent collusion between the TPA and the CSP. The experimental results show that our scheme has higher audit efficiency.
Recommendation based on attributes and social relationships

Attributes are important auxiliary information for representing user and item features, especially in data-sparse scenario, and can serve as their main source. However, different attributes have different importance for representing users or items, and users have different preferences for different attributes of an item, which is often ignored in current research. With the rapid development of social network platforms, the use of users' social relationships to improve and enhance the performance of recommender systems has also received considerable attention. However, most studies have treated the influence of different social relationships in the same way. Different social friends have different influences on users' preferences. To solve these problems, this study proposes a recommendation model based on attributes and social relationships. In the model, we use attributes to learn the feature representations of users and items, while we use social relationships to augment the feature representations of users. We developed two attention mechanisms: attribute-level and social-level. The former was used to distinguish the relative importance of different attributes, and the latter was used to model the influence of different social relationships. The novel recommendation model in this study innovatively combines attribute information and social relationships to improve feature representation learning of users and items, and to alleviate the problem of data sparsity. Extensive experiments on public datasets have confirmed the effectiveness of the proposed model, and comparisons with different baselines showed significant improvement in recommendation accuracy.
Class attention to regions of lesion for imbalanced medical image recognition

Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In this paper, we propose a simple yet effective framework, named Class Attention to REgions of the lesion (CARE), to handle data imbalance issues by embedding attention into the training process of Convolutional Neural Networks (CNNs). The proposed attention module helps CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn their characteristics more effectively. In addition, this attention module works only during the training phase and does not change the architecture of the original network, so it can be directly combined with any existing CNN architecture. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. To alleviate the need for manual annotation, we further developed variants of CARE by leveraging the traditional saliency methods or a pretrained segmentation model for bounding box generation. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework with manual bounding box annotations. A series of experiments on an imbalanced skin image dataset and a pneumonia dataset indicates that our method can effectively help the network focus on the lesion regions of rare diseases and remarkably improves the classification performance of rare diseases.
Magnification-arbitrary depth super-resolution with multiscale consistency deformable alignment

For many years, depth map super-resolution has been a problem worthy of studying in 3D reconstruction. Although many excellent algorithms have been developed to solve this problem, depth map super-resolution at any scale has not attracted much attention. In this paper, we propose a magnification-arbitrary depth super-resolution method based on Meta-SR framework. Different from the single spatial location guidance in Meta-SR, the multiscale consistency alignment module learns a hierarchical registered discontinuities and semantic feature guidance to aggregate more structural information of the depth map and its corresponding color image. Specially, the dense pyramid deformable convolution realizes the scale expansion and improves the precision of sub-pixel upsampling from coarse to fine. A collaborative geometrical-spatial attention based weight prediction module is proposed to measure the correlation between geometrical features, spatial location and the upsampling weights, and predict a set of weight filters for reconstruction. A large number of experiments show that our method achieves satisfactory subjective and objective results on both integral and fractional scales compared with existing methods.
StawGAN: Structural-Aware Generative Adversarial Networks for Infrared Image Translation

This paper addresses the problem of translating night-time thermal infrared images, which are the most adopted image modalities to analyze night-time scenes, to daytime color images (NTIT2DC), which provide better perceptions of objects. We introduce a novel model that focuses on enhancing the quality of the target generation without merely colorizing it. The proposed structural aware (StawGAN) enables the translation of better-shaped and high-definition objects in the target domain. We test our model on aerial images of the DroneVeichle dataset containing RGB-IR paired images. The proposed approach produces a more accurate translation with respect to other state-of-the-art image translation models. The source code will be available after the revision process.