Reviewerly/exHarmony huggingface dataset:
Random Forest for Semantic Segmentation Using Pre Trained CNN (VGG16) Features

For years the semantic segmentation of images has become a high-level task, and a great challenge in the field of computer vision. From a technical point of view, semantic segmentation consists in segmenting an image by grouping the parts that belong to the same class of object (for example: beach, road, trees…). Today, several research works are proposed to treat this research axis, and still the challenge remains to have a powerful model to improve or augment these existing techniques. In this paper, we have proposed a hybrid method for semantic image segmentation, combining two popular and powerful techniques. These are the convolutional neural network CNN with the VGG16 model and the Random Forest algorithm, which are two outstanding classifiers. In fact, first of all we apply for the input image the pre-trained model based on the CNN architecture which is VGG16 to automatically extract the features. Then, these extracted features are used for another classification process with Random Forest to make the prediction of the labels of the input models. The proposed approach combines the strengths of two techniques which explains the improvement and accuracy of the obtained results. To make a comparison between the different methods, we adopted a set of evaluation measures: IOU, SSIM, SAM, and MS-SSIM. From the results, we can say that our approach shows its effectiveness in solving the problem of image segmentation in general.
Fractional Stochastic Search Algorithms: Modelling Complex Systems via AI

The aim of this article is to establish a stochastic search algorithm for neural networks based on the fractional stochastic processes {BtH,t≥0} with the Hurst parameter H∈(0,1). We define and discuss the properties of fractional stochastic processes, {BtH,t≥0}, which generalize a standard Brownian motion. Fractional stochastic processes capture useful yet different properties in order to simulate real-world phenomena. This approach provides new insights to stochastic gradient descent (SGD) algorithms in machine learning. We exhibit convergence properties for fractional stochastic processes.
A Novel Approach for Electronic Medical Records Based on NFT-EMR

saving the patient medical record electronically is done via electronic medical records (EMRs) where all the patient-related information that consider private and sensitive is usually related to the treatment process, as well as the diagnoses process of a specific patient usually this information needs to be shared and re-submit by many peers doctors with each, In this paper, propose a novel approach for electronic medical records system using a non-fungible token (NFT) based on a customized permission blockchain built with secret sharing technology to reduce the key management overhead as well as provide a readable extension of the ledger to ensure it is easy to read and easy access from any type of computer. The system that uses NFT for electronic medical records (NFT-EMR) ensures easy access and guarantees availability, privacy, and security providing authority to the patient over his data as well as proof of ownership which is done using proof of secret shares and consistency this system can be used in real-world between hospitals and medical centers and within metaverse world. The NFT-ERM system was evaluated using the main evaluation factors used to evaluate blockchain and compared to the standard ERM system.
Proactive Task Offloading for Load Balancing in Iterative Applications

Abstract Load imbalance is often a challenge for applications in parallel systems. Static cost models and pre-partitioning algorithms distribute the load at the beginning. Nevertheless, dynamic changes during execution or inaccurate cost indicators may lead to imbalance at runtime. Reactive work-stealing strategies can help monitor the execution and perform task migration to balance the load. However, the benefits depend on migration overhead and assumption about future execution. Our proactive approach further improves existing solutions by applying machine learning to online load prediction. Following that, we propose a fully distributed algorithm for adapting the prediction result to guide task offloading. The experiments are performed with an artificial test case and a realistic application named Sam(oa) $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> on three systems with different communication overhead. Our results confirm improvements for important use cases compared to previous solutions. Furthermore, this approach can support co-scheduling tasks across multiple applications.
Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches

The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images.
A CLSTM network algorithm for automatic modulation recognition

Aiming at the problems of low recognition accuracy and inexplicable features extracted by neural networks in signal modulation recognition algorithms based on deep learning, this paper proposes a CLSTM recognition algorithm based on IQ signal features. The algorithm first uses a 2×1 convolution kernel to extract the phase feature of the signal, then uses a multiple convolution layer to extract the temporal feature of the signal, and then outputs the result of the convolution layer to the LSTM module to further extract the temporal feature, finally, the recognition result is output by the fully connected layer and the recognition effect is improved by optimizing the network structure. The experimental results show that on the RML2016.10a public dataset, when the SNR is higher than 0 dB, the recognition accuracy of the algorithm reaches 89.21%, which is 15.02% and 9.85% higher than the classical CNN2 and CLDNN network algorithms.
The Inclusion Degrees of Fuzzy Skill Maps and Knowledge Structures

Fuzzy skills can provide a representation of the latent cognitive abilities of individuals in terms of proficiency. A disjunctive model of the fuzzy skill map, in which individuals master at least one skill to reach the minimum requirement, always delineates a knowledge space. At the same time, a conjunctive model delineates a simple closure space, which needs individuals' proficiency of all skills to reach the minimum requirements. The restriction of the disjunctive model is too weak, and the conjunctive model is too strongly constrained. Between these two models, there is a lack of detailed classification on different numbers of skills that meet the minimum requirements. Thus, for a given fuzzy skill map (Q,S,τ), it is natural for us to propose the concept of a fuzzy skill inclusion degree, which is a ratio of the number of skills meeting the minimum requirements to the number of skills needed to solve the item q. This ratio can describe levels of different numbers of skills mastered, and it is the basis of the variable precision α-models in the following discussion. By the fuzzy skill inclusion degree, the variable precision α-models can divide knowledge structures into different levels between the disjunctive model and the conjunctive model. Meanwhile, both the disjunctive and conjunctive models become two special cases of the variable precision α-model. The knowledge structures delineated via disjunctive models are classified into different levels by α. The dual to the knowledge structure delineated via the variable precision α-model is also discussed. For a fuzzy skill multimap (Q,S,μ), the variable precision α-competency models of a fuzzy skill multimap are found, and different knowledge structures can be obtained through different values of α. When α=1, the variable precision α-competency model becomes a competency model of the fuzzy skill multimap.
New closed‐form efficient estimator for multivariate gamma distribution

Maximum likelihood estimation is used widely in classical statistics. However, except in a few cases, it does not have a closed form. Furthermore, it takes time to derive the maximum likelihood estimator (MLE) owing to the use of iterative methods such as Newton–Raphson. Nonetheless, this estimation method has several advantages, chief among them being the invariance property and asymptotic normality. Based on the first approximation to the solution of the likelihood equation, we obtain an estimator that has the same asymptotic behavior as the MLE for multivariate gamma distribution. The newly proposed estimator, denoted as , is also in closed form as long as the ‐consistent initial estimator is in the closed form. Hence, we develop some closed‐form ‐consistent estimators for multivariate gamma distribution to improve the small‐sample property. is an alternative to MLE and performs better compared to MLE in terms of computation time, especially for large datasets, and stability. For the bivariate gamma distribution, the is over 130 times faster than the MLE, and as the sample size increasing, the is over 200 times faster than the MLE. Owing to the instant calculation of the proposed estimator, it can be used in state–space modeling or real‐time processing models.
A Federated Learning Benchmark for Drug-Target Interaction

Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests [5, 18]. This work proposes the application of federated learning, which is reconcilable with the industry’s constraints. It does not require sharing any information that would reveal the entities’ data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15% improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data and the cost of adding more clients.
Vibration Suppression Trajectory Planning of Underwater Flexible Manipulators Based on Incremental Kriging-Assisted Optimization Algorithm

It is of great significance to expand the functions of submarines by carrying underwater manipulators with a large working space. To suppress the flexible vibration of underwater manipulators, an improved sparrow search algorithm (ISSA) combining an elite strategy and a sine algorithm is proposed for the trajectory planning of underwater flexible manipulators. In this method, the vibration evaluation function is established based on the precise dynamic model of the underwater flexible manipulator and considering complex motion and vibration constraints. Simulation results show that the ISSA algorithm requires only 1/3.68 of the time of PSO. Compared to PSO, SSA and the opposition-based learning sparrow search algorithm (OBLSSA), the optimization performance is improved by 17.3%, 13.1% and 9.7%, respectively. However, because the complex dynamics model of the underwater flexible manipulator leads to large computational effort and a long optimization time, ISSA is difficult to apply directly in practice. To obtain a large number of optimization results in a shorter time, an incremental Kriging-assisted ISSA (IKA-ISSA) is proposed in this paper. Simulation results show that IKA-ISSA has good nonlinear approximation ability and the optimization time is only 3% of that of the ISSA.
The Evolution of Narrativity in Abstracts of the Biomedical Literature between 1989 and 2022

Previous analysis has shown that the use of narrative devices in the biomedical literature has changed over time. The purpose of the present study was to measure the degree of narrativity in corpora of scientific abstracts obtained from Pubmed through the use of a proprietary software LIWC 2022, which, based on pre-set dictionaries, attributes scores for Staging, Plot Progression and Cognitive Tension to texts. Each text is automatically divided into a number of segments, so that the score change can be assessed throughout the different parts of a text, thus identifying its narrative arc. We systematically applied the scoring system to a corpus of 680,000 abstracts from manuscripts of any kind and genre published in the years 1989–2022 and indexed in MEDLINE, an independent corpus of 680,000 abstracts of Primary studies published in the same years, and finally a corpus of 680,000 abstracts of Review papers that appeared in the 1989–2022 interval. We were able to create plots of the pattern of how these three scores changed over time in each corpus and observed that the prototypical pattern observed in narrative texts, e.g., novels, is not seen in abstracts of the scientific literature, which, however, mostly possess a diverse but quite reproducible pattern. Overall, Reviews better conform to a higher degree of narrativity than Primary studies.
Decomposed Two-Stage Prompt Learning for Few-Shot Named Entity Recognition

Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in few-shot classification tasks, adapting to token-level classification similar to the NER task presents challenges in terms of time consumption and efficiency. In this work, we propose a decomposed prompt learning NER framework for few-shot settings, decomposing the NER task into two stages: entity locating and entity typing. In training, the location information of distant labels is used to train the entity locating model. A concise but effective prompt template is built to train the entity typing model. In inference, a pipeline approach is used to handle the entire NER task, which elegantly resolves time-consuming and inefficient problems. Specifically, a well-trained entity locating model is used to predict entity spans for each input. The input is then transformed using prompt templates, and the well-trained entity typing model is used to predict their types in a single step. Experimental results demonstrate that our framework outperforms previous prompt-based methods by an average of 2.3–12.9% in F1 score while achieving the best trade-off between accuracy and inference speed.
Analysis of the innovation path of university education management informatization in the era of big data

Abstract In order to have a qualitative improvement in the education management of universities in the background of the era of big data technology, it is necessary to integrate information technology and technological means in the process of education management. Based on this background, this paper constructs an informatization education management platform. In the process of platform architecture, the cloud storage module obtains the exact nearest neighbors by the time factor, optimizes the construction process by using the weighting function and hybrid coordination filtering algorithm, and simplifies the teaching management process. In the teaching management system, the fixed rules of the feature selection algorithm are used to achieve teaching resource selection prediction in the optimal feature subset and establish the best classification surface. Finally, the performance of the platform proposed in this paper is verified from two levels: resource recommendation and education management. The experimental results show that the MAE (Mean Absolute Error) value of this platform is always under 0.7, with a mean value of 0.523, and the experimental teaching resource recommendation effect is better than that of the IE virtual experiment teaching platform and data platform. And the maximum number of visits that the platform can bear is 884, and the maximum amount of government processing can reach 839, which is much larger than the number of 800 users of experimental teaching in the university and fully meets the needs of university education management.
Cross-stream contrastive learning for self-supervised skeleton-based action recognition

Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augmentations of 3D skeleton within a single data stream, which merely leverages the easy positive pairs and limits the ability to explore the complicated movement patterns. In this paper, we advocate that the defect of single-stream contrast and the lack of necessary feature transformation are responsible for easy positives, and therefore propose a Cross-Stream Contrastive Learning framework for skeleton-based action Representation learning (CSCLR). Specifically, the proposed CSCLR not only utilizes intra-stream contrast pairs, but introduces inter-stream contrast pairs as hard samples to formulate a better representation learning. Besides, to further exploit the potential of positive pairs and increase the robustness of self-supervised representation learning, we propose a Positive Feature Transformation (PFT) strategy which adopts feature-level manipulation to increase the variance of positive pairs. To validate the effectiveness of our method, we conduct extensive experiments on three benchmark datasets NTU-RGB + D 60, NTU-RGB + D 120 and PKU-MMD. Experimental results show that our proposed CSCLR exceeds the state-of-the-art methods on a diverse range of evaluation protocols.
An FPGA-based online reconfigurable CNN edge computing device for object detection

Edge devices offer advantages such as low computation latency and high data security for executing convolutional neural networks (CNNs). However, deploying CNNs on resource-constrained devices is challenging due to the high computational intensity of CNNs and limited hardware on-chip resources. This hinders the application of deep learning techniques on edge devices. To address this issue, this paper proposes a reconfigurable CNN edge computing system based on Field-Programmable Gate Array (FPGA) for target detection tasks. The system utilizes the pipeline structure of FPGAs to speed up network computation and employs off-chip memory to store network models. Thus, the need for tiling techniques with high intermediate cache requirements was circumvented in this system. Additionally, we developed a parallel data scheduling model to reduce storage access cost delay on network computation efficiency. Our model achieves comparable efficiency with on-chip storage-based works when using off-chip storage. Online reconfigurable design enables the system to configure network structure and parameters at runtime to achieve different target recognition. This provides greater flexibility for use cases with frequently changing requirements. The proposed system was implemented on a Spartan-6 XC6SLX150 FPGA platform and was applied to pedestrian and vehicle classification tasks. To evaluate performance, speed, power consumption, and average intersection over union (IoU) were separately measured. Our system achieved a detection speed of 16 frames per second (FPS) on the Spartan-6, with a power consumption rate of 0.79 W and an average IoU of 41.2%. Remarkably, the system demonstrated a 178% speed increase and a 60% power consumption reduction compared to the FPGA-based FitNN implementation, while classification accuracy was reduced by only 2.52%.
Quantitative assessment of blockchain applications for Industry 4.0 in manufacturing sector

Blockchain is one of the emerging digital technologies that will play a role in the breakthroughs of the fourth industrial revolution. The use of blockchain technology has the potential to greatly benefit businesses of all sizes by increasing their data's integrity, privacy, and openness. The term "Industry 4.0″ refers to the amalgamation of recent advances in manufacturing technology that have helped businesses cut production times significantly. The industrial and supply chain industries can benefit from these technological advancements in a number of ways. Increased efficiency in production and a more stable supply chain are just two of the many benefits that blockchain promises to bring to the manufacturing industry. The study focuses on Blockchain's huge potential in the context of Industry 4.0. Understanding the role of Blockchain technology in Industry 4.0 is examined, along with its various drivers, enablers, and associated capabilities. The several sub-domains of Industry 4.0 that can benefit from the implementation of Blockchain technology are also covered. The present research is primary and exploratory in nature. The sample size of the study is 256. The responses obtained from workers working in manufacturing sector in Delhi/NCR. The responses from workers obtained through structured questionnaire. The several sub-domains of Industry 4.0 found that can benefit from the implementation of Blockchain technology. At last, the existing study found the most important uses of Blockchain technology in the fourth industrial revolution.