Reviewerly/exHarmony huggingface dataset:
MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion

Significant progress has been made in generating counterfeit images and videos. Forged videos generated by deepfaking have been widely spread and have caused severe societal impacts, which stir up public concern about automatic deepfake detection technology. Recently, many deepfake detection methods based on forged features have been proposed. Among the popular forged features, textural features are widely used. However, most of the current texture-based detection methods extract textures directly from RGB images, ignoring the mature spectral analysis methods. Therefore, this research proposes a deepfake detection network fusing RGB features and textural information extracted by neural networks and signal processing methods, namely, MFF-Net. Specifically, it consists of four key components: (1) a feature extraction module to further extract textural and frequency information using the Gabor convolution and residual attention blocks; (2) a texture enhancement module to zoom into the subtle textural features in shallow layers; (3) an attention module to force the classifier to focus on the forged part; (4) two instances of feature fusion to firstly fuse textural features from the shallow RGB branch and feature extraction module and then to fuse the textural features and semantic information. Moreover, we further introduce a new diversity loss to force the feature extraction module to learn features of different scales and directions. The experimental results show that MFF-Net has excellent generalization and has achieved state-of-the-art performance on various deepfake datasets.
A multi-component PSO algorithm with leader learning mechanism for structural damage detection

The particle swarm optimization (PSO) algorithm has been widely used to solve optimization problems. One of its main drawbacks is a slow and sometimes premature convergence, which traps the swarm in a local minimum. Several PSO variants have been proposed to alleviate this phenomenon. Still, when dealing with structural damage detection (SDD), the performances of these algorithms are not homogeneous and highly depend on the number of defects and their severities, which even lead them to require assistance from other methods. This paper proposes a multi-component PSO with cooperative learning named MuC-PSO. Instead of resorting to other methods, a strategy pool is constructed in the proposed MuC-PSO by combining four PSO variants, which guarantees that the algorithm can execute multiple search strategies collaboratively. Moreover, a leader learning mechanism (LLM) is also implemented to ensure information exchange and lead the global convergence of the method. This strategy allows the PSO variants to benefit from each other and enables the MuC-PSO to solve complex SDD problems. The performances of the MuC-PSO are evaluated in nine damage scenarios of single and multiple damages with severity levels between [10%,50%]. Our algorithm is compared with different recent optimization algorithms, including the four PSO variants alone and some non-PSO algorithms. The simulation results on three types of damages clearly demonstrate the effectiveness and superiority of the MuC-PSO.
A Verifiable Arbitrated Quantum Signature Scheme Based on Controlled Quantum Teleportation

In this paper, we present a verifiable arbitrated quantum signature scheme based on controlled quantum teleportation. The five-qubit entangled state functions as a quantum channel. The proposed scheme uses mutually unbiased bases particles as decoy particles and performs unitary operations on these decoy particles, applying the functional values of symmetric bivariate polynomial. As such, eavesdropping detection and identity authentication can both be executed. The security analysis shows that our scheme can neither be disavowed by the signatory nor denied by the verifier, and it cannot be forged by any malicious attacker.
Adiabatic Quantum-Flux-Parametron: A Tutorial Review

The adiabatic quantum-flux-parametron (AQFP) is an energy-efficient superconductor logic element based on the quantum flux parametron. AQFP circuits can operate with energy dissipation near the thermodynamic and quantum limits by maximizing the energy efficiency of adiabatic switching. We have established the design methodology for AQFP logic and developed various energy-efficient systems using AQFP logic, such as a low-power microprocessor, reversible computer, single-photon image sensor, and stochastic electronics. We have thus demonstrated the feasibility of the wide application of AQFP logic in future information and communications technology. In this paper, we present a tutorial review on AQFP logic to provide insights into AQFP circuit technology as an introduction to this research field. We describe the historical background, operating principle, design methodology, and recent progress of AQFP logic.
On the adoption of blockchain for business process monitoring

Abstract Being the blockchain and distributed ledger technologies particularly suitable to create trusted environments where participants do not trust each other, business process management represents a proper setting in which these technologies can be adopted. In this direction, current research work primarily focuses on blockchain-oriented business process design, or on execution engines able to enact processes through smart contracts. Conversely, less attention has been paid to study if and how blockchains can be beneficial to business process monitoring. This work aims to fill this gap by (1) providing a reference architecture for enabling the adoption of blockchain technologies in business process monitoring solutions, (2) defining a set of relevant research challenges derived from this adoption, and (3) discussing the current approaches to address the aforementioned challenges.
Digital Competencies for New Journalistic Work in Media Outlets: A Systematic Review

Media organizations operate in a rivalry-charged ecosystem nowadays, as a consequence of emerging patterns of news production, distribution, and consumption. Furthermore, the growing of public social media manifestations and the arrival of digital journalism require new professional roles, responsibilities, and skills inside the media industry. In this context, Faculties of Communication need to equip students with the digital competencies that are relevant to new media outlets and journalistic work. Based on this approach, the main objective of this study is to answer the following questions: What does the literature suggest about the digital skills that new professional profiles should acquire in the field of journalism? Which dimensions of digital competence are gaining visibility and which dimensions are being neglected? To answer the scientific objectives, a systematic review has been carried out following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The application of the two models of digital competence, Bloom’s taxonomy (1956), and digital competence in education (Redecker, 2017), serves as a framework in two ways: to determine the level of digital competence development, and to identify the dimensions on which greater emphasis is being placed. The results show a lack of studies linked to key aspects of digital competence, especially those related to personal growth, emotional state (Redecker, 2017), and the development of a deep level of acquisition of this competence (Bloom, 1956). This article proposes to reflect on whether we want to train professionals according to the model demanded by the media outlets nowadays, or whether we prefer to train communication professionals with a deep level of digital competence, since they are able to respond to the future and changing needs of the 21st century.
Integration of Deep Sparse Autoencoder and Particle Swarm Optimization to Develop a Recommender System

Recommender systems are known as intelligent systems which have many applications in enormous domains such as social networks, e-commerce services, and online shopping. Deep neural networks have shown significant improvement in the performance of recommender systems by learning the latent features of users/items based on input data. However, it is a challenging issue to how to apply deep neural networks on different resources and how to integrate their results. In this regard, we propose a recommender system in this paper based on deep sparse autoencoder and particle swarm optimization. In particular, a deep sparse autoencoder is utilized to learn latent features based on the ratings matrix, trust relationships, and tag information. Then, particle swarm optimization is used to find the optimal weights of these latent features in calculating unknown ratings. Experiments on two datasets show the superiority of the proposed method in comparison with state of the art recommender algorithms.
Progressive edge-sensing dynamic scene deblurring

Abstract Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many factors. In recent years, the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively studied. We have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data streams. Our new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred edges. The coarse-to-fine architecture restores the image structure, first performing global adjustments, and then performing local refinement. In this way, not only is global correlation considered, but also residual information is used to significantly improve image restoration and enhance texture details. Experimental results show quantitative and qualitative improvements over existing methods.
Connecting reservoir computing with statistical forecasting and deep neural networks

Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.
Software defect prediction based on nested-stacking and heterogeneous feature selection

Abstract Software testing guarantees the delivery of high-quality software products, and software defect prediction (SDP) has become an important part of software testing. Software defect prediction is divided into traditional software defect prediction and just-in-time software defect prediction (JIT-SDP). However, most of the existing software defect prediction frameworks are relatively simplified, which makes it extremely difficult to provide developers with more detailed reference information. To improve the effectiveness of software defect prediction and realize effective software testing resource allocation, this paper proposes a software defect prediction framework based on Nested-Stacking and heterogeneous feature selection. The framework includes three stages: data set preprocessing and feature selection, Nested-Stacking classifier, and model classification performance evaluation. The novel heterogeneous feature selection and nested custom classifiers in the framework can effectively improve the accuracy of software defect prediction. This paper conducts experiments on two software defect data sets (Kamei, PROMISE), and demonstrates the classification performance of the model through two comprehensive evaluation indicators, AUC, and F1-score. The experiment carried out large-scale within-project defect prediction (WPDP) and cross-project defect prediction (CPDP). The results show that the framework proposed in this paper has an excellent classification performance on the two types of software defect data sets, and has been greatly improved compared with the baseline models.
Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Applied sensing technology has made it possible for human beings to experience a revolutionary aspect of the science and technology world. Along with many other fields in which this technology is working wonders, human locomotion activity recognition, which finds applications in healthcare, smart homes, life-logging, and many other fields, is also proving to be a landmark. The purpose of this study is to develop a novel model that can robustly handle divergent data that are acquired remotely from various sensors and make an accurate classification of human locomotion activities. The biggest support for remotely sensed human locomotion activity recognition (RS-HLAR) is provided by modern smartphones. In this paper, we propose a robust model for an RS-HLAR that is trained and tested on remotely extracted data from smartphone-embedded sensors. Initially, the system denoises the input data and then performs windowing and segmentation. Then, this preprocessed data goes to the feature extraction module where Parseval’s energy, skewness, kurtosis, Shannon entropy, and statistical features from the time domain and the frequency domain are extracted from it. Advancing further, by using Luca-measure fuzzy entropy (LFE) and Lukasiewicz similarity measure (LS)–based feature selection, the system drops the least-informative features and shrinks the feature set by 25%. In the next step, the Yeo–Johnson power transform is applied, which is a maximum-likelihood-based feature optimization algorithm. The optimized feature set is then forwarded to the multilayer perceptron (MLP) classifier that performs the classification. MLP uses the cross-validation technique for training and testing to generate reliable results. We designed our system while experimenting on three benchmark datasets namely, MobiAct_v2.0, Real-World HAR, and Real-Life HAR. The proposed model outperforms the existing state-of-the-art models by scoring a mean accuracy of 84.49% on MobiAct_v2.0, 94.16% on Real-World HAR, and 95.89% on Real-Life HAR. Although our system can accurately differentiate among similar activities, excessive noise in data and complex activities have shown an inverse effect on its performance.
Somewhat Homomorphic Encryption: Ring Learning with Error Algorithm for Faster Encryption of IoT Sensor Signal-Based Edge Devices

In recent years, Homomorphic Encryption (HE) has shown the possibility of securely running a computation arbitrarily without performing the data decryption. Many authors have shown Somewhat Homomorphic Encryption (SHE) or Fully Homomorphic Encryption (FHE) schemes implemented practically on both the addition and multiplication operations for SHE. The recent methods for implementing the FHE methods completely depend on arbitrarily reducing the time taken to perform the encrypted multiplication operation to increase the computation power required by SHE methods. This paper aims to accelerate the encryption primitives in an integer-based SHE based on the duration between each data transmission from the sensor and data packaging method. If the number of sensors increases exponentially in an edge device environment, the signals have to be encrypted faster in a packed mode in the edge environment and transferred to the cloud without a loss in data. The presented SHE method reduces the time taken for encryption based on the input number from the sensor and invariably increases the performance of the edge device. This advantage also helps the deploying healthcare application obtain end-to-end privacy in transmitting sensitive patient data.
Learning transfer feature representations for gas path fault diagnosis across gas turbine fleet

Intelligent data-driven fault diagnosis based on conventional machine learning techniques has been extensively studied in recent years. However, these methods often assumed that the data used for training and testing are drawn from the identical distribution, which is impractical in real application. Such idealized hypothesis may confine these promising data-driven techniques to well-designed experimental environments rather than actually putting them into real-world applications. In practice, the distribution discrepancies between source domain and target domain will degrade the diagnostic performance. To this end, this work introduces a transfer learning based extreme learning machine to align the distribution discrepancies of the data collected from a turbofan engine, which is rarely studied in the fault diagnosis for aero-engine. The proposed method is capable of learning the transferable cross domain features while preserving the properties and structures of source domain as much as possible. Meanwhile, the marginal distribution and conditional distribution discrepancies are matched. Through these transfer data representations, a relatively high diagnostic accuracy is guaranteed. Finally, extensive experiments have been performed on gas path fault diagnosis of turbofan engine, including hybrid transfer cases and complete transfer cases, to verify the effectiveness and feasibility of the proposed method.
Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th Edition

"Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th Edition." Journal of Electronic Resources in Medical Libraries, 19(1-2), pp. 54–55
Automatic Coronary Artery Segmentation of CCTA Images With an Efficient Feature-Fusion-and-Rectification 3D-UNet

Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolutional neural network. The proposed method consists of three steps. First, to improve the efficiency and effectiveness of the segmentation, a 2D DenseNet classification network is utilized to screen out the non-coronary-artery slices. Second, we propose a coronary artery segmentation network based on the 3D-UNet, which is capable of extracting, fusing and rectifying features efficiently for accurate coronary artery segmentation. Specifically, in the encoding process of the 3D-UNet network, we adapt the dense block into the 3D-UNet so that it can extract rich and representative features for coronary artery segmentation; In the decoding process, 3D residual blocks with feature rectification capability are applied to improve the segmentation quality further. Third, we introduce a Gaussian weighting method to obtain the final segmentation results. This operation can highlight the more reliable segmentation results at the center of the 3D data blocks while weakening the less reliable segmentations at the block boundary when merging the segmentation results of spatially overlapping data blocks. Experiments demonstrate that our proposed method achieves a Dice Similarity Coefficient (DSC) value of 0.826 on a CCTA dataset constructed by us. The code of the proposed method is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/alongsong/3D_CAS</uri> .
Best practices for photovoltaic performance loss rate calculations

Abstract The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simplifies the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across different types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using different pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for differentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.
Threshold image segmentation based on improved sparrow search algorithm

Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants' update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows' update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.
An image encryption algorithm using the combination of chaotic maps

Nowadays, as the science and technology are advanced, users are more willing to use the visual data and also such kind of data in medical and military applications. Therefore, unauthorized access to personal and confidential information has become a critical problem in today's digital world. Encryption is an appropriate method for fixing such problems. Due to high size of data and high correlation among the image's adjacent pixels, texts encryption algorithms are not good for image encryption. In designing an algorithm for the future generations, three principles should be met: firstly, algorithm should be so simple that it is evaluated and analyzed easily, accurately and completely. Secondly, an encryption algorithm should have a security margin much more than the required for current identified attacks. Thirdly, in design, the tools and ideas already known and reliable should be utilized as much as possible. Hence, in this paper, an image encryption algorithm is proposed by combination of two Henon chaos and Logistic systems. Experimental results confirm and demonstrate that the proposed method yields better outcomes in the field of security against the statistical and differential attacks compared to the original AES encryption algorithm and previous modified encryption algorithms.