Reviewerly/exHarmony huggingface dataset:
A Novel CS 2G-starlet denoising method for high noise astronomical image

In deep space exploration, astronomical images are easily contaminated by noise, which affects image quality. However, for the denoising of high-noise astronomical images, obtaining good reconstruction results from the current denoising methods is difficult. This paper proposes a novel compressed sensing (CS) 2G-starlet denoising (NCSSD) method can effectively improve the reconstruction quality of astronomical images. Firstly, the proposed 2G-starlet Wiener filtering operator is used to select these starlet coefficients in the process of sparse image transformation. Then, a novel iterative stepsize operator is proposed to enhance the convergence ability of the algorithm. At the same time, an optimized cycle spinning method is proposed to adjust the reconstructed image. Through extensive simulation experiments and deep theoretical analysis and verification, the effectiveness of this method in high-noise astronomical image denoising is fully proven, and the quality of reconstructed image is improved. The proposed method has been successfully interconnected with the telescope and industrial camera, realizing the rapid acquisition of high-quality astronomical images. The research results of this article have important value for promoting the effective acquisition and analysis of astronomical images in exploring deep space.
Will the biopsychosocial model of medicine survive in the age of artificial intelligence and machine learning?

the biomedical model of medicine was replaced by the biopsychosocial model in order to better accommodate psychological and social aspects of illness. The introduction of machine learning techniques provides the perspective of truly personalized medicine. This poses new challenges to our medical model. to explore the implications of personalized medicine for the biopsychosocial model. scholarly reflection. The ability of machine learning technology to integrate a wide diversity of data makes it possible to develop predictive models for presentation, course and treatment response in individual patients. Such models are based on individual risk factors and protective factors that may have diverging influences in different individuals. In a medical model adjusted to accommodate the possibilities of personalized medicine, it should be possible to highlight the importance and impact of each single factor in each individual patient. At present, the biopsychosocial model is not well prepared for this. When adopting machine learning technology in clinical practice, new skills and expertise will be required from physicians. They should be able to weigh and explain algorithms supported decisions to their patients. Moreover, new research should be designed in such a way that data will be suited for machine learning and can be integrated with existing databases in order to increase their size and scope. Currently, the biopsychosocial model is not well prepared to accommodate the possibilities of personalized medicine. Adaptations are needed to deal with the highly individual aspects of the patient's disease.
Research on the construction and implementation of power grid fault handling knowledge graphs

Power grid fault disposal has important guiding significance for the efficient and orderly emergency work of power grid accidents. The knowledge graph technology is used to extract, express and manage the fault disposal information. It is also used to assist dispatchers in fault handling, which can effectively improve the power grid emergency handling capacity and dispatching intelligence level. The unstructured content in the form of a large number of texts in the power grid dispatching operation link is condensed into a structured knowledge network that can be expressed, operated and reasoned. For example, operating procedures, disposal plans, dispatching rules, etc. A top-down and bottom-up knowledge graph construction method for power grid fault handling is proposed. The knowledge graphs are divided into three categories: entity graph of power grid equipment, logic graph of fault handling and case graph. The data and technical means required for graph construction are described. In the process of practice, the knowledge organization structure, notion types, and relationships between notions of the knowledge graphs are defined to form the model layer of knowledge graph. Finally, according to the function realization of each link, the solution ideas and key technologies are analyzed. So as to realize fault information analysis and discrimination based on knowledge atlas, intelligent aided decision-making and multi-dimensional human–computer interaction.
Application of Project-Based Learning to a Software Engineering course in a hybrid class environment

This paper centers on Project-Based Learning (PBL). In PBL, the student is now the center of the whole teaching and learning process, while the instructor‘s role is now of a facilitator presenting to the students the resources and guidance to solve the given problem. Most existing studies, apply PBL to courses having in-person students. The paper presents the application of a PBL approach to a Software Engineering (SE) course having a hybrid class environment (i.e., online and in-person students). The main objective of this paper is to analyze the students’ attitudes after experiencing working on a real-life problem as part of our PBL approach in a hybrid class environment. We propose a relaxed plan-based software development model as basis for guiding the project execution. At the end of the course, we applied a survey to the students to evaluate their experience in the course. We obtained the answers of 70.8% of students taking a SE course. With these answers, we could measure the students’ perception of using PBL in a SE course and how this strategy helped them to gain soft and hard skills in software development. We divided the answers for their analysis into different categories: soft skills, technical skills, learning experience, and other results. Moreover, we compare the performance of the teams and students based on their type (i.e., online and in-person). We found qualitative differences in the experience of online and in-person students. Based on our experience with this study, we provide guidelines for applying PBL in a hybrid environment. Overall, our study has demonstrated a positive contribution in supporting teaching SE using a PBL in a hybrid class environment.
First-order quantum phase transition in the squeezed Rabi model

Abstract Quantum phase transition and entanglement in the Rabi model with a squeezed light are investigated. We find a special unitary-transformation method that removes the nonintegrable squeezing and counterrotating-wave interactions when the qubit frequency is close to the field frequency. The analytical ground state agrees well with the numerical solution. We demonstrate that the ground state exhibits a first-order quantum phase transition at a critical point induced linearly by the squeezed light. This quantum phase transition requires neither multiple qubits nor an infinite ratio of qubit frequency to field frequency, which solves a critical problem for the theory and experiment in Rabi model. As the qubit-field coupling strength increases, the ground-state entanglement reaches its maximum value at the critical point.
A reliable and fair federated learning mechanism for mobile edge computing

Federated learning-enabled mobile edge computing implements privacy-preserving collaborative machine learning of complex models. However, mobile end devices have high mobility and are vulnerable to be corrupted due to exposuring to an open network environment, which not only affect the performance of federated learning models but also bring serious security issues. To solve the above problem, we propose a reliable and fair federated learning mechanism for mobile edge computing. To begin with, in order to select end nodes as reliable as possible, a reputation-based end nodes selection scheme is proposed, which includes the construction of the reputation model and the concealment of selected end nodes. Then, corresponding counter strategies are proposed for the possible attack behaviors launched by corrupted end nodes during the training process. To avoid the impact of poisoning attacks on the model performance, a new global model aggregation strategy is proposed to maintain the model performance by meritocratic campaigning. To solve the fairness problem caused by end nodes’ escape, a reward-penalty scheme using blockchain is proposed. Finally, the numerical results clearly show that the proposed mechanism is effective for reliable and fair federated learning for mobile edge computing, maintaining high accuracy under attacks from malicious end nodes while penalizing them to ensure fairness.
Affinity-aware resource provisioning for long-running applications in shared clusters

Resource provisioning plays a pivotal role in determining the right amount of infrastructure resource to run applications and reduce the monetary cost. A significant portion of production clusters is now dedicated to long-running applications (LRAs), which are typically in the form of microservices and executed in the order of hours or even months. It is therefore practically important to plan ahead the placement of LRAs in a shared cluster for the minimized number of compute nodes required by them. Existing works on LRA scheduling are often application-agnostic, without particularly addressing the constraining requirements imposed by LRAs, such as co-location affinity constraints and time-varying resource requirements. In this paper, we present an affinity-aware resource provisioning approach for deploying large-scale LRAs in a shared cluster subject to multiple constraints, with the objective of minimizing the number of compute nodes in use. We investigate a broad range of solution algorithms which fall into three main categories: Application-Centric, Node-Centric, and Multi-Node approaches, and tune them for typical large-scale real-world scenarios. Experimental studies driven by the Alibaba Tianchi dataset show that our algorithms can achieve competitive scheduling effectiveness and running time, as compared with the heuristics used by the latest work including Medea and LraSched. Best results are obtained by the Application-Centric algorithms, if the algorithm's running time is of primary concern, and by Multi-Node algorithms, if the solution quality is of primary concern.
Self-progress aggregate learning for weakly supervised salient object detection

Abstract The task of salient object detection aims to find the most salient object from the samples. In the field of weakly supervised learning, the existing weakly supervised salient object detection (WSSOD) methods often fail to utilize the limited label information (such as self-affinity or edge features, and scale transform) for learning. Therefore, this paper proposes a self-progress aggregate learning method named SPAL. First, the feature optimization scheme of the edge information module is put forward based on analysis of the problems existing in the current convolutional neural network for detection of the edge information of the object. Obviously, the salient object has a low requirement for high-level information, In particular, in order to improve the utilization rate of the network structure without increasing its complexity, the affinity global context is design in view of the particularity of the structure of a salient object. The structure of a salient object not only depends on the deep-level semantic feature information to a certain extent, but also has a certain guiding effect on the object position and edge information. Second, high-level affinity information is used to complement the slight-level edge information globally, and the scale attention module is adopted to guide the network to adapt the multi-scale reinforcement feature learning ability for the salient object regions. Our method SPAL achieved better experimental results than the other competitive models for comparison on five benchmark data sets (i.e. for DUTS-TEST, compared with CCFNet our method achieved an improvement of 0.6% for mean absolute error (MAE), 5.1% for F b , and 1.1% for E ℰ ), which demonstrates the effectiveness of our proposed method.
Information transmission with continuous variable quantum erasure channels

Quantum capacity, as the key figure of merit for a given quantum channel, upper bounds the channel&amp;apos;s ability in transmitting quantum information. Identifying different types of channels, evaluating the corresponding quantum capacity, and finding the capacity-approaching coding scheme are the major tasks in quantum communication theory. Quantum channel in discrete variables has been discussed enormously based on various error models, while error model in the continuous variable channel has been less studied due to the infinite dimensional problem. In this paper, we investigate a general continuous variable quantum erasure channel. By defining an effective subspace of the continuous variable system, we find a continuous variable random coding model. We then derive the quantum capacity of the continuous variable erasure channel in the framework of decoupling theory. The discussion in this paper fills the gap of a quantum erasure channel in continuous variable setting and sheds light on the understanding of other types of continuous variable quantum channels.
A Decentralized COVID-19 Vaccine Tracking System Using Blockchain Technology

Coronavirus disease 2019 (COVID-19) vaccines play a crucial role in preventing the spread of the disease. However, the circulation of low-quality and counterfeit vaccines seriously affects human health and the reputation of real vaccine manufacturers (VMs) and increases the amount of fear concerning vaccination. In this study, we address this problem by developing a blockchain-based COVID-19 vaccine tracking system called “Vacchain”. Our Vacchain allows users (USERs) to track and trace the route of vaccines. We propose three mechanisms, namely, a system manager (SYS-MAN), a mutual agreement concerning vaccine ownership, and vaccine passports, to enhance the security and reliability of data recorded in the Vacchain ledger. We develop this system on the Substrate platform with the Rust language. Our implementation, evaluation, and analysis have shown that Vacchain can trace and track vaccines smoothly. In addition, data security and reliability are enhanced by the abovementioned three mechanisms. The proposed system is expected to contribute to preventing the spread of COVID-19.
A Rubik's Cube Cryptosystem-based Authentication and Session Key Generation Model Driven in Blockchain Environment for IoT Security

Over the past decade, IoT has gained huge momentum in terms of technological exploration, integration, and its various applications even after having a resource-bound architecture. It is challenging to run any high-end security protocol(s) on Edge devices. These devices are highly vulnerable toward numerous cyber-attacks. IoT network nodes need peer-to-peer security, which is possible if there exists proper mutual authentication among network devices. A secure session key needs to be established among source and destination nodes before sending the sensitive data. To generate these session keys, a strong cryptosystem is required to share parameters securely over a wireless network. In this article, we utilize a Rubik's cube puzzle-based cryptosystem to exchange parameters among peers and generate session key(s). Blockchain technology is incorporated in the proposed model to provide anonymity of token transactions, on the basis of which the network devices exchange services. A session key pool randomizer is used to avoid network probabilistic attacks. Our hybrid model is capable of generating secure session keys that can be used for mutual authentication and reliable data transferring tasks. Cyber-attacks resistance and performance results were verified using standard tools, which gave industry level promising results in terms of efficiency, light weightiness, and practical applications.
Causal Discovery via Causal Star Graphs

Discovering causal relationships among observed variables is an important research focus in data mining. Existing causal discovery approaches are mainly based on constraint-based methods and functional causal models (FCMs). However, the constraint-based method cannot identify the Markov equivalence class and the functional causal models cannot identify the complex interrelationships when multiple variables affect one variable. To address the two aforementioned problems, we propose a new graph structure Causal Star Graph (CSG) and a corresponding framework Causal Discovery via Causal Star Graphs (CD-CSG) to divide a causal directed acyclic graph into multiple CSGs for causal discovery. In this framework, we also propose a generalized learning in CSGs based on a variational approach to learn the representative intermediate variable of CSG’s non-central variables. Through the generalized learning in CSGs, the asymmetry in the forward and backward model of CD-CSG can be found to identify the causal directions in the directed acyclic graphs. We further divide the CSGs into three categories and provide the causal identification principle under each category in our proposed framework. Experiments using synthetic data show that the causal relationships between variables can be effectively identified with CD-CSG and the accuracy of CD-CSG is higher than the best existing model. By applying CD-CSG to real-world data, our proposed method can greatly augment the applicability and effectiveness of causal discovery.
Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems

The artificial Gorilla Troop Optimization (GTO) algorithm (GTO) is a metaheuristic optimization algorithm that simulates the social life of gorillas. This paper proposes three innovative strategies considering the GTO algorithm’s insufficient convergence accuracy and low convergence speed. First, a shrinkage control factor fusion strategy is proposed to expand the search space and reduce search blindness by strengthening the communication between silverback gorillas and other gorillas to improve global optimization performance. Second, a sine cosine interaction fusion strategy based on closeness is proposed to stabilize the performance of silverback gorillas and other gorilla individuals and improve the convergence ability and speed of the algorithm. Finally, a gorilla individual difference identification strategy is proposed to reduce the difference between gorilla and silverback gorillas to improve the quality of the optimal solution. In order to verify the optimization effect of the modified artificial gorilla troop optimization (MGTO) algorithm, we used 23 classic benchmark functions, 30 CEC2014 benchmark functions, and 10 CEC2020 benchmark functions to test the performance of the proposed MGTO algorithm. In this study, we used a total of 63 functions for algorithm comparison. At the same time, we carried out the exploitation and exploration balance experiment of 30 CEC2014 and 10 CEC2020 functions for the MGTO algorithm. In addition, the MGTO algorithm was also applied to test seven practical engineering problems, and it achieved good results.
Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.
Research on Improved Wavelet Threshold Denoising Method for Non-Contact Force and Magnetic Signals

In order to solve the problem of noise interference in the collected magneto mechanical signals, a new wavelet shrinkage threshold based on adaptive estimation is proposed. Based on the shortcomings of the traditional threshold function, an improved threshold function is proposed, and the parameters of the threshold function are solved by the improved genetic algorithm, and the optimal denoising effect is finally obtained. The new threshold function can not only make up the defects of each threshold function, ensure the continuity of threshold function, but also flexibly adjust the threshold to adapt to different noise conditions, and solve the deviation caused by inherent threshold function, and protect the useful information with noise signals.
Leveraging Active Learning for Failure Mode Acquisition

Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations which require significant computing resources. With the recent advances in Natural Language Processing (NLP), efforts have been made to automate this process. However, it is not only time consuming, but extremely challenging to obtain maintenance records that list failure modes. Unsupervised learning methods such as topic modeling, clustering, and community detection are promising approaches for automatic processing of maintenance records to identify failure modes. However, the nascent state of NLP tools combined with incompleteness and inaccuracies of typical maintenance records pose significant technical challenges. As a step towards addressing these challenges, this paper proposes a framework in which online active learning is used to identify failure modes from maintenance records. Active learning provides a semi-supervised machine learning approach, allowing for a human in the training stage of the model. The hypothesis of this paper is that the use of a human to annotate part of the data and train a machine learning model to annotate the rest is more efficient than training unsupervised learning models. Results demonstrate that the model is trained with annotating less than ten percent of the total available data. The framework is able to achieve ninety percent (90%) accuracy in the identification of failure modes in test cases with an F-1 score of 0.89. This paper also demonstrates the effectiveness of the proposed framework with both qualitative and quantitative measures.