Reviewerly/exHarmony huggingface dataset:
Data sharing in energy systems

Big data has been advocated as a dominant driving force to unleash the great waves of the next-generation industrial revolution. While the ever-increasing proliferation of heterogeneous data contributes to a more sustainable energy system, considerable challenges remain for breaking down the barrier of data sharing across monopolistic sectors and fully exploiting data asset value in a trustworthy environment. Here, we focus on a global aspiration and interest regarding the challenges, techniques, and prospects of data sharing in energy systems. In this paper, a conceptual framework for data sharing is designed, in which we introduce the commodity attribute of data assets and explain the bottlenecks of data trading. Two critical issues, i.e., right confirmation and privacy protection, are then systematically reviewed, which provide a fundamental guarantee for credible data openness. A detailed data market is conceived by elaborating on market bids, data asset valuation and pricing strategy, and game-based clearing. Finally, we conduct a discussion about some low-hanging fruit of data sharing in energy systems.
PPFchain: A novel framework privacy-preserving blockchain-based federated learning method for sensor networks

Internet of Things (IoT) has been widely used in many smart applications such as smart cities, smart agriculture, healthcare, industry, etc. In addition, the importance of IoT-based architectures has increased with the emergence of innovative technologies such as 5G networks and quantum computing. Electrochemical sensors (ECS) have recently been used in IoT-based architectures thanks to their easy integration into smart systems in many fields such as pharmaceutical, food, medical diagnosis, clinical, genetic analysis, wearable, forensic identification, and monitoring of environmental variables. However, Although IoT-based systems have ensured the availability of sensor data in traditional architecture, there are many challenges, such as low latency, availability, real-time data traceability, and security. In addition, new challenges regarding security and privacy have emerged in the system as the ever-growing smart connected IoT devices generate a significant amount of heterogeneous data. Given these challenges, in this paper, we propose PPFchain, a new federated learning-enabled blockchain-based framework to ensure the security and privacy of sensor-IoT-based architectures using sampled ECS data. PPFchain has a lightweight, low-cost, high-performance architecture in the IoT-based blockchain network. In the architecture, we used the federated model and cryptographic primitives for user and data privacy in off-chain fog nodes considering performance. Moreover, we compared with traditional blockchain models using various performance metrics for system performance in a distributed architecture, such as an event and storage-based smart contract. In addition, the results show that the PPFchain provides accuracy, efficiency, and enhanced security.
D<sup>2</sup>UNet: Dual Decoder U-Net for Seismic Image Super-Resolution Reconstruction

Super-resolution reconstruction is an essential task of seismic inversion due to the low resolution and strong noise of field data. Popular deep networks derived from U-Net lack the ability to recover detailed edge features and weak signals. In this paper, we propose a dual decoder U-Net (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> UNet) to explore both detail and edge information of the data. The encoder inputs the low resolution image and the edge image obtained through the Canny algorithm. Edge image can provide rich shape and boundary information, which is helpful to generate more accurate and high-quality data. The dual decoder consists of a main decoder for high-resolution recovery and an edge decoder for edge contour detection. These two decoders interact with a texture warping module (TWM) with deformable convolution. TWM aims to distort realistic edge details to match the fidelity of low resolution inputs, especially the location of edges and weak signals. The loss function is a combination of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> loss and multi-scale structural similarity loss (MS-SSIM) to ensure perception quality. Results on synthetic and field seismic images show that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> UNet not only improves the resolution of noisy seismic images, but also maintains the image fidelity.
Quantum neuronal sensing of quantum many-body states on a 61-qubit programmable superconducting processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.
A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction

Methods based on artificial neural network (ANN) or deep neural network (DNN) applications have been proposed to predict job cycle time effectively. However, the predicting mechanism of ANNs (or DNNs) is often difficult to understand and communicate. This problem has hindered their acceptability (and practicability). Furthermore, existing explainable artificial intelligence (XAI) techniques use simpler decision rules to approximate the prediction process and/or outcomes of ANNs (or DNNs). However, these decision rules may not conform to domain knowledge, which limits their usefulness. This study proposes a modified random forest incremental interpretation (RFII) approach to address this issue. In the proposed methodology, the input–output relationship of an ANN (or DNN) is fitted by decision rules using a random forest approach. Then, domain knowledge is extracted, and decision rules are checked against this knowledge. Decision rules with poor conformance to domain knowledge are excluded. Subsequently, the remaining decision rules are aggregated in a way that is convenient for user interpretation. The modified RFII method has been applied to a real case in the literature to demonstrate its applicability.
Ranking teaching–learning-based optimization algorithm to estimate the parameters of solar models

As one of the most promising renewable energies, solar energy can be converted to electricity through photovoltaic (PV) systems. It is indispensable to identify the parameters of PV systems with the aim of controlling and simulating. Thanks to the complexity of PV systems, parameter identification is still a challenging task. In this paper, we develop a Ranking Teaching–Learning-Based​ Optimization (RTLBO) to solve the problem, in which Teaching–Learning-Based​ Optimization (TLBO) is a population-based swarm algorithm and mimics the learning process in a classroom. RTLBO ranks learners into superior and inferior groups, in which the outstanding learners learn from the top three agents to boost the local search. In contrast, the low learners learn from each other by guidance. The two phases are in parallel to balance the local and global search. The proposed RTLBO is used to extract parameters of different models, including the single diode model, double diode model and three PV module models. TLBO, four TLBO variants, and fifteen meta-heuristic algorithms are selected as the rivals of RTLBO. Several experiments have shown that our method is a reliable and effective algorithm when addressing the parameters of PV systems.
Improved Secure Communication Mechanism for IoT Platform Devices

In this research work, proposed a secure communication mechanism in Internet-of-Things (IoT), where blockchain technology has been presented as a potential security measure. The next section discusses the background of the proposed study, which mainly briefs about the security issues in an IoT, existing security approaches in an IoT, and Blockchain technology's evolution as an appropriate security measure for secured communication in an IoT. The proposed study's core aim is to construct an intelligent framework that can streamline an IoT's communication demands more securely for resisting lethal adversaries. Security in the communication process is developed by harnessing the potential features of blockchain.
Detecting Phishing Domains Using Machine Learning

Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature.
Do supply chain related factors enhance the prediction accuracy of blockchain adoption? A machine learning approach

This study employs a machine learning approach to examine whether and to what extent supply chain related factors can improve the prediction accuracy of blockchain technology (BT) adoption. The supply chain factors studied include supply chain collaboration, information sharing along the supply chain, partner power, trust in supply chain partners and Guanxi with supply chain partners. We choose the Technology-Organization-Environment (TOE) framework as the benchmark model and quantify the importance of supply chain factors by comparing the prediction accuracy of the benchmark model using only the TOE framework with an extended model combining supply chain factors with the TOE framework. Based on data collected from 629 Chinese firms, we find that Support Vector Machine stands out among all machine learning algorithms: the complete model including both supply chain and TOE factors reaches an accuracy rate of 89.3 %, while the model including only TOE factors has an accuracy rate of 83 %. Based on a 10-fold cross-validated paired t-test, we further confirm that incorporating supply chain factors can significantly improve the prediction accuracy by 6.34 % over the benchmark model. Our results indicate that TOE factors are insufficient to understand and predict BT adoption; supply chain factors also need to be considered.
Evolving Marine Predators Algorithm by dynamic foraging strategy for real-world engineering optimization problems

The Marine Predators Algorithm (MPA) is a novel hunting-based optimizer. The MPA’s central concept is based on the well-known Lévy Flight (LF) and Brownian Motion (BM) strategies as well as a simple transition model between these two strategies. The canonical MPA proposes three static steps to tune the transition behavior between the LF and BM strategies. Although MPA provides exemplary performance in many test functions, the discrete transition between the two mentioned phases causes it to get stuck in local optima when faced with real-world optimization problems. In order to address this shortcoming, this paper proposes a soft dynamic transition between LF and BM to model this encounter naturally, considering the continuous nature of the transition between LF and BM in marine predators’ real life. In order to evaluate the performance of the developed Dynamic Foraging Strategy MPA (DFSMPA), twenty-nine optimization test functions, thirty complex CEC-BC-2017 functions, ten benchmarks of CEC06-2019 test suit, and ten real applicable engineering problems, including power system design, synthesis and process design, industrial chemical producer, power-electronic design, mechanical design, and animal feed ratio, are employed. The DFSMPA is evaluated against four groups of standard optimization approaches, including (1) Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Equilibrium Optimizer (EO), Niching Chimp Optimization Algorithm (ChOA), Henry Gas Solubility Optimization (HGSO) as recent optimization algorithms, (2) Lévy Flight GWO (LGWO) and Evolutionary Algorithms with Adaptive Lévy Mutations (EALM) as the two best dynamic Lévy-based optimization algorithms, (3) SHADE, CMA-ES, and LSHADESPACMA as the three state-of-the-art optimization algorithms, and jDE100, DISHchain1e+12, CIPDE, and EBOwithCMAR as best performing algorithms in IEEE CEC06-2019 competition. Three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni–Dunn and Holm, and Friedman average rank tests, are utilized to perform a comprehensive assessment. The results show that the DFSMPA achieved the first rank among 46 out of 70 benchmark functions and engineering problems and exhibited similar results compared with SHADE and CMA-ES in other benchmarks. The statistical analysis demonstrated that DFSMPA is a significantly superior optimizer than the three first categories’ benchmark algorithms, while its result is statistically similar to jDE100, DISHchain1e+12.
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations

The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge. Previous approaches to this problem have not prioritized the reduction of feature map redundancy, but the introduction of Ghost modules represents a major innovation in this area. Ghost modules use a series of inexpensive linear transformations to extract additional feature maps from a set of intrinsic features, allowing for a more comprehensive representation of the underlying information. GhostNetV1 and GhostNetV2, both of which are based on Ghost modules, serve as the foundation for a group of lightweight face recognition models called GhostFaceNets. GhostNetV2 expands upon the original GhostNetV1 by adding an attention mechanism to capture long-range dependencies. Evaluation of GhostFaceNets using various benchmarks reveals that these models offer superior performance while requiring a computational complexity of approximately 60-275 MFLOPs. This is significantly lower than that of State-Of-The-Art (SOTA) big convolutional neural network (CNN) models, which can require hundreds of millions of FLOPs. GhostFaceNets trained with the ArcFace loss on the refined MS-Celeb-1M dataset demonstrate SOTA performance on all benchmarks. In comparison to previous SOTA mobile CNNs, GhostFaceNets greatly improve efficiency for face verification tasks. The GhostFaceNets entire code will be made available after publication.
An Efficient End-to-End Obstacle Avoidance Path Planning Algorithm for Intelligent Vehicles Based on Improved Whale Optimization Algorithm

End-to-end obstacle avoidance path planning for intelligent vehicles has been a widely studied topic. To resolve the typical issues of the solving algorithms, which are weak global optimization ability, ease in falling into local optimization and slow convergence speed, an efficient optimization method is proposed in this paper, based on the whale optimization algorithm. We present an adaptive adjustment mechanism which can dynamically modify search behavior during the iteration process of the whale optimization algorithm. Meanwhile, in order to coordinate the global optimum and local optimum of the solving algorithm, we introduce a controllable variable which can be reset according to specific routing scenarios. The evolutionary strategy of differential variation is also applied in the algorithm presented to further update the location of search individuals. In numerical experiments, we compared the proposed algorithm with the following six well-known swarm intelligence optimization algorithms: Particle Swarm Optimization (PSO), Bat Algorithm (BA), Gray Wolf Optimization Algorithm (GWO), Dragonfly Algorithm (DA), Ant Lion Algorithm (ALO), and the traditional Whale Optimization Algorithm (WOA). Our method gave rise to better results for the typical twenty-three benchmark functions. In regard to path planning problems, we observed an average improvement of 18.95% in achieving optimal solutions and 77.86% in stability. Moreover, our method exhibited faster convergence compared to some existing approaches.
Joint Multiclass Object Detection and Semantic Segmentation for Autonomous Driving

Object detection and semantic segmentation are two fundamental problems in autonomous driving systems.As recent studies have illustrated the strong correlation between the two tasks, the joint development of object detection and semantic segmentation tasks by utilizing end-to-end encoder-decoder architecture has gained popularity in recent years.However, context information loss is a common problem for simple encoder-decoder systems.Considering this problem, this study proposes a joint multi-class object detection and semantic segmentation method with the addition of a modern feature fusion mechanism to prevent context information loss.The experiments are conducted on our proposed large-scale Inha Computer Vision 2022 (ICV22) dataset that was specifically collected and annotated for object detection and semantic segmentation tasks.The proposed model achieves 87.2 mIoU performance on the introduced ICV22 dataset with 92.1 accuracy for the segmentation task, which is far superior to that of the DeeplabV3++ semantic segmentation method.Additionally, joint object detection and semantic segmentation model illustrated 40.2 mAP for object detection task and 56.4 mIoU for semantic segmentation task, outperforming previously introduced methods on publicly available Cityscapes dataset with real-time inference speed of 42 FPS on NVIDIA RTX 3090 GPU.
Optimal task sequencing and aborting in multi-attempt multi-task missions with a limited number of attempts

Mission abort policies have been investigated for both single-attempt and multi-attempt missions in the last decade. The existing models typically assumed a single task performed during the mission. However, a mission in practice may consist of multiple tasks (e.g., a surveillance mission consisting of multiple tasks with different routes). This paper advances the state of the art on aborting policies (AP) by modeling systems performing a mission with multiple tasks. Each task may be executed under a different environment and have a distinct AP based on the number of shocks experienced and on an operation time threshold. Each task may be attempted multiple times and the total number of attempts is limited by the available system resource. The operating environments during the operation phase and the rescue phase of each task may also differ. The task-dependent AP and the execution sequence of multiple tasks are jointly modeled and optimized to minimize the expected mission losses (EML). The solution methodology encompasses a new recursive EML evaluation algorithm and the genetic algorithm-based optimization method. The proposed AP model and solution method are demonstrated using a case study of an unmanned aerial vehicle performing a five-task surveillance mission.
Wireless End-to-End Image Transmission System using Semantic Communications

Semantic communication is considered the future of mobile communication, which aims to transmit data beyond Shannon's theorem of communications by transmitting the semantic meaning of the data rather than the bit-by-bit reconstruction of the data at the receiver's end.The semantic communication paradigm aims to bridge the gap of limited bandwidth problems in modern high-volume multimedia application content transmission.Integrating AI technologies with the 6G communications networks paved the way to develop semantic communication-based end-to-end communication systems.In this study, we have implemented a semantic communication-based end-to-end image transmission system, and we discuss potential design considerations in developing semantic communication systems in conjunction with physical channel characteristics.A Pre-trained GAN network is used at the receiver as the transmission task to reconstruct the realistic image based on the Semantic segmented image at the receiver input.The semantic segmentation task at the transmitter (encoder) and the GAN network at the receiver (decoder) is trained on a common knowledge base, the COCO-Stuff dataset.The research shows that the resource gain in the form of bandwidth saving is immense when transmitting the semantic segmentation map through the physical channel instead of the ground truth image in contrast to conventional communication systems.Furthermore, the research studies the effect of physical channel distortions and quantization noise on semantic communication-based multimedia content transmission.
Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline

Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure quantities, such as vibrations, cutting forces, temperature, currents, power consumption, and acoustic emission, to diagnose defects and enable zero-defect manufacturing as part of the Industry 4.0 vision. The continuity of high-quality sensor data streams is fundamental to predicting phenomena, such as geometric deformations, surface roughness, excessive coolant use, and imminent tool wear with adequate accuracy and appropriate timing. However, in practice, data acquired by some sensors can be of poor quality and unsuitable for prediction due to sensor faults stemming from environmental factors. In this paper, we answer if we can repair erroneous data in a faulty sensor based on data simultaneously available in redundant sensors that observe the same process. We present a machine learning pipeline to synthesize virtual sensors that can step in for faulty sensors to maintain reasonable quality and continuity in sensor data streams. We have validated the synthesized virtual sensors in four industrial case studies.