Reviewerly/exHarmony huggingface dataset:
Benchmarking framework for anomaly localization: Towards real-world deployment of automated visual inspection

Localizing defects in products is a critical component of industrial pipelines in manufacturing, retail, and many other industries to ensure consistent delivery of high quality products. Automated anomaly localization systems leveraging computer vision have the potential to replace laborious and subjective manual inspection of products. Recently, there have been tremendous efforts in this research domain investigating deep learning-based anomaly localization methods. However, such proposed methods, mainly considering product-specific evaluation, cannot be directly implemented for real-world use. Therefore, despite the advancements, there is still a gap between research and deployment of those methods to real-world production environments. Implementing any automated solution for manufacturing can involve a steep upfront investment. It is important to develop an industry-friendly benchmarking framework to ensure the feasibility and robustness of an automated quality control system. In this paper, we present a new anomaly localization benchmarking framework considering different aspects - (1) understand the performance of models in a generalizable product-agnostic manner, (2) explore pros and cons to find the most optimal modeling approach, (3) develop an efficient training and inference scheme with defect-free training samples and very few defective samples for evaluation, and (4) perform an ablation study of threshold estimation techniques to determine optimal threshold level for segmentation. We release a newly-labeled dataset for the research community with product-agnostic categorization of defective product images. To the best of our knowledge, this is the first anomaly localization work on developing a benchmarking framework focusing on real-world use. We believe domain experts from different industries will find this useful and can gain valuable insights to deploy automated visual inspection in production pipelines.
Multi agent deep reinforcement learning for resource allocation in <scp>container‐based</scp> clouds environments

Abstract Virtualization enables the deployment of several virtual servers on the same physical layer, critical component of the cloud. As cloud services advance, more apps that use repositories are developed, which adds to the overburden. Containers have evolved into the most reliable and lightweight virtualization technology for cloud services thanks to their flexible sorting, mobility, and scalability. In container‐based clouds, containers can potentially cut data centre energy usage more than virtual machines (VMs) do. Containers are less energy intensive than VMs. Resource allocation is the most prevalent method in cloud systems. However, resource allocation in container‐based clouds (RAC) is innovative and complicated due to its two‐level architecture. This includes the pairing of virtual machines and physical computers with containers. In cloud container services, planner components are essential. This lowers expenses while improving the performance and variety of workloads using cloud resources. The cloud infrastructure resource allocation framework is gaining popularity since it is energy‐efficient and focuses on cloud data management to maximize income and minimize costs. In this paper, we proposed a deep learning‐based architecture capable of achieving high data centre energy efficiency and preventing Service Level Agreement (SLA) violations from deploying green cloud resources. This research describes a hybrid optimum and multi‐agent deep reinforcement learning (MADRL) technique for dynamic task scheduling (DTS) in a container cloud environment. The MADRL‐DTS model for the RAC problem considers VM overheads, VM types, and an affinity restriction. Then, to address the RAC issue, we develop a DTS hyper‐heuristic technique. MADRL‐RAC may give allocation rules by recognizing workload trends and VM types from previous workload traces. Compared to modern procedures, the results demonstrate a significant reduction in energy consumption. The evaluation for energy‐efficient resource allocation is tested in several virtualized environments to get a high power usage effectiveness and CPU usage.
A benchmark dataset and evaluation methodology for Chinese zero pronoun translation

The phenomenon of zero pronoun (ZP) has attracted increasing interest in the machine translation community due to its importance and difficulty. However, previous studies generally evaluate the quality of translating ZPs with BLEU score on MT testsets, which is not expressive or sensitive enough for accurate assessment. To bridge the data and evaluation gaps, we propose a benchmark testset and evaluation metric for target evaluation on Chinese ZP translation. The human-annotated testset covers five challenging genres, which reveal different characteristics of ZPs for comprehensive evaluation. We systematically revisit advanced models on ZP translation and identify current challenges for future exploration. We release data, code, and trained models, which we hope can significantly promote research in this field.
Information technologies in the vocational secondary education system: inoculation experience

Introduction. This article is devoted to the problem of finding optimal ways to include information technologies in the educational process. There is a definite potential for the use of information technologies in productive teaching methods. At the same time, there is a problem of refusing to use them primarily within the framework of informative and illustrative methods. Purpose. The study of the potential of the use of information technologies in education in the system of secondary vocational training. Materials and methods. The methodological component is represented by testing, comparative analysis, interpretation and generalization. The specifics of creating a practical training program as a basis for evaluating the effectiveness of the information technology inoculation in teaching activities at the college are substantiated. Results. The lesson was designed using the problem method and the method of individual projects. The entire cycle of pedagogical work was implemented and described in detail: from a lesson design to evaluating its effectiveness. Discussion and conclusion. The conducted lesson is evaluated as effective since students have demonstrated significant progress in understanding the topic and mastering skills during the initial control and final testing. However, the result of practical approval indicates some reassessment of students' knowledge of the completed program. There is a need for additional improvement of motivating components that contribute to the activation of creative potential and initiative of students. It was shown that the use of the problem method and the method of individual projects significantly contributes to the activation of cognitive and creative activity compared to the use of informative methods. The results of the work conducted can be considered a reference point for further theoretical and practical research in the field of effective use of information technologies in education.
Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach

The Internet of Things (IoT) has revolutionized the world with its diverse applications and smart connected devices.These IoT devices communicate with each other without human intervention and make life easier in many ways.However, the independence of these devices raises several significant concerns, such as security and privacy preservation due to malicious and compromised nodes within the network.Trust management has been introduced as a less computationally intensive alternative to traditional approaches such as cryptography.The proposed FedTrust approach addresses these challenges by designing a method for identifying malicious and compromised nodes using federated learning.FedTrust trains edge nodes with a provided dataset and forms a global model to predict the abnormal behavior of IoT nodes.The proposed approach utilizes a novel trust dataset consisting of 19 trust parameters from three major components: knowledge, experience, and reputation.To reduce the computational burden, FedTrust employs the concept of communities with dedicated servers to divide the dataset into smaller parts for more efficient training.The proposed approach is extensively evaluated in comparison to existing approaches in terms of accuracy, precision, and other metrics to validate its performance in IoT networks.Simulation results demonstrate the effectiveness of FedTrust by achieving a higher rate of detection and prediction of malicious and compromised nodes.
Computer Vision Techniques for Hand Gesture Recognition: Survey

Hand gesture recognition has recently emerged as a critical component of the human-computer interaction (HCI) concept, allowing computers to capture and interpret hand gestures. In addition to their use in many medical applications, communication between the hearing impaired, device automation, and robot control, hand gestures are of particular importance as a form of nonverbal communication. So far, hand gesture recognition has taken two approaches and relied on a variety of technologies; the first on sensor technology and the second on computer vision. Given the importance of hand gesture recognition applications and technology development today, the importance of the research lies in shedding light on the latest techniques used in the recognition and interpretation of hand gestures. A survey on the techniques used from 2017–2022 has been presented in this research, with a focus on the computer vision approach. The survey was carried out as follows: the first part dealt with research based on artificial intelligence techniques for hand gesture recognition, and the second part focused on research that used artificial neural networks and deep learning for hand gesture recognition.
Comparative evaluation of automated machine learning techniques for breast cancer diagnosis

Breast cancer is the second leading cause of death among women worldwide. Early detection is crucial for a high possibility of recovery, but current diagnostic procedures rely on visual inspection by doctors, which is time-consuming, requires consultation, and lacks reliable automatic detection systems. Therefore, an automatic diagnosis system based on machine learning (ML) models is highly required to improve the accuracy of detection and prediction. In this study, an efficient classification method based on ML techniques was proposed to help doctors diagnose and distinguish between malignant and benign tumors relevant to breast cancer prediction. Seven AutoML techniques, including Orange, Lazy Predict, TPOT, MLJAR, MATLAB classification learner, and AutoKeras, were examined and applied to eight different datasets to evaluate their performance in terms of classification accuracy. The experimental results demonstrate a significant improvement using state-of-the-art techniques, with Lazy Predict and MATLAB Classification Learner outperforming all other ML techniques for binary classification tasks. The contribution of this work is the identification of the best ML model among multiple models inside each AutoML technique applied to different breast cancer datasets. All datasets and codes used in this study are freely available from https://github.com/amrrashed/breast-cancer-datasets-and-codes.
Context-aware transformer for image captioning

Recently, image captioning models have made remarkable progress by introducing transformer architecture, which utilizes self-attention to explore intra- and inter-modal interactions. However, most existing methods only consider region-level characteristic during the attention weight calculation and ignore the image-level information. This seriously hinders the whole model from understanding the scene content. In this paper, we propose a Context-Aware Transformer (CATNet) with two novel designs, namely Context Augmented Attention (CAA) and Dual Way Controller (DWC). Concretely, CAA in encoder enables the extraction of more comprehensive visual representation through modeling the communications between multi-level visual features. DWC in decoder is used to enhance the fusion between visual features and language representation through utilizing complementarity of global context and local regions. Extensive experiments conducted on MSCOCO dataset show that the proposed CATNet has achieved state-of-the-art performance on both Karpathy test set and online test.
Predictive hierarchical reinforcement learning for path-efficient mapless navigation with moving target

Deep reinforcement learning (DRL) has been proven as a powerful approach for robot navigation over the past few years. DRL-based navigation does not require the pre-construction of a map, instead, high-performance navigation skills can be learned from trial-and-error experiences. However, recent DRL-based approaches mostly focus on a fixed navigation target. It is noted that when navigating to a moving target without maps, the performance of the standard RL structure drops dramatically on both the success rate and path efficiency. To address the mapless navigation problem with moving target, the predictive hierarchical DRL (pH-DRL) framework is proposed by integrating the long-term trajectory prediction to provide a cost-effective solution. In the proposed framework, the lower-level policy of the RL agent learns robot control actions to a specified goal, and the higher-level policy learns to make long-range planning of shorter navigation routes by sufficiently exploiting the predicted trajectories. By means of making decisions over two level of policies, the pH-DRL framework is robust to the unavoidable errors in long-term predictions. With the application of deep deterministic policy gradient (DDPG) for policy optimization, the pH-DDPG algorithm is developed based on the pH-DRL structure. Finally, through comparative experiments on the Gazebo simulator with several variants of the DDPG algorithm, the results demonstrate that the pH-DDPG outperforms other algorithms and achieves a high success rate and efficiency even though the target moves fast and randomly.
Intensive Care Unit readmission prediction with correlation enhanced multi-task learning

Prediction for Intensive Care Unit (ICU) readmission is conducive to assisting doctors in treatment-related decision making and reducing the risk of relapse after discharge. Recently, existing ICU readmission prediction approaches train each sub-task independently, which prevents the models from using complementary information between these sub-tasks. In this paper, we propose correlation enhanced Multi-Task learning with Pearson and RNN-based Neural Ordinary Differential Equations Model (MP-ROM). In order to enhance the learning of general features and avoid the local optima in single-task training, we construct the Shared-Bottom structure of multi-task learning, which enables multiple tasks to share model structure and parameters. Besides, we add the task correlation score calculated by Pearson correlation calculation, enhancing the association between sub-tasks. Experiment results on MIMIC-III dataset show that MP-ROM achieves the highest average precision and demonstrates that task association enhanced can further improve the predictive performance of ICU readmission risk.
A robust control approach to asymptotic optimality of the heavy ball method for optimization of quadratic functions

Among first order optimization methods, Polyak’s heavy ball method has long been known to guarantee the asymptotic rate of convergence matching Nesterov’s lower bound for functions defined in an infinite-dimensional space. In this paper, we use results on the robust gain margin of linear uncertain feedback control systems to show that the heavy ball method is provably worst-case asymptotically optimal when applied to quadratic functions in a finite dimensional space.
Marine predators algorithm: A comprehensive review

Marine predators algorithm (MPA) is a recently proposed metaheuristic algorithm that mimics the marine predators behavior when attacking their preys. Recently, the MPA has been broadly employed to tackle numerous optimization problems in various research areas and has confirmed its supremacy over a large number of the metaheuristic algorithms regard to convergence speed and accuracy thanks to its simplicity, flexible implementation and few adjustable parameters requirements. A comprehensive review of the MPA is presented in this paper along with its variants such as binary, discrete, modifications, hybridizations, chaotic, quantum and multi-objective versions. This paper also reviews various applications of MPA in electrical engineering, computer science, medicine, etc. Moreover, further research directions for MPA are suggested. The source code of the MPA can be found at: http://www.alimirjalili.com/MPA.html.
Total-aware suppressed possibilistic c-means clustering

Cutset-type possibilistic c-means (CPCM) is an improved version of possibilistic c-means (PCM), and it addresses the issue of consistency clustering in PCM. However, the clustering performance has not significantly improved, and its convergence speed is still slow. Hence, a total-aware suppressed possibilistic c-means (TSPCM) clustering is proposed in this paper. Firstly, from a global perspective, the typicality values of all the non-winners of samples inside the cluster core are decreased by the suppressed factor while increasing the typicality values of all the winners of samples in the same core. Meanwhile, the typicality values of samples outside the cluster core remain unchanged. The corrected typicality values are used to update the clustering centers, and the modified clustering centers are again used to renew the typicality values alternately until the algorithm converges. Secondly, the objective function of the proposed TSPCM is constructed, and Zangwill's theorem firmly establishes its convergence. Finally, two robust total-aware suppressed algorithms are presented when TSPCM is used for the clustering of numerical data with noise and the segmentation of noisy images. Experimental results show that the proposed TSPCM and its robust algorithms are obviously superior to existing state-of-the-art PCM-related algorithms.
Construction of cardiovascular information extraction corpus based on electronic medical records

&lt;abstract&gt;&lt;p&gt;Cardiovascular disease has a significant impact on both society and patients, making it necessary to conduct knowledge-based research such as research that utilizes knowledge graphs and automated question answering. However, the existing research on corpus construction for cardiovascular disease is relatively limited, which has hindered further knowledge-based research on this disease. Electronic medical records contain patient data that span the entire diagnosis and treatment process and include a large amount of reliable medical information. Therefore, we collected electronic medical record data related to cardiovascular disease, combined the data with relevant work experience and developed a standard for labeling cardiovascular electronic medical record entities and entity relations. By building a sentence-level labeling result dictionary through the use of a rule-based semi-automatic method, a cardiovascular electronic medical record entity and entity relationship labeling corpus (CVDEMRC) was constructed. The CVDEMRC contains 7691 entities and 11,185 entity relation triples, and the results of consistency examination were 93.51% and 84.02% for entities and entity-relationship annotations, respectively, demonstrating good consistency results. The CVDEMRC constructed in this study is expected to provide a database for information extraction research related to cardiovascular diseases.&lt;/p&gt;&lt;/abstract&gt;
FPGA-based acceleration for binary neural networks in edge computing

As a core component in intelligent edge computing, deep neural networks (DNNs) will increasingly play a critically important role to address the intelligence-related issues in the industry domain, like smart factories and autonomous driving. Due to the requirement for a large amount of storage space and computing resources, DNNs are unfavorable for resource-constrained edge computing devices, especially for mobile terminals with scarce energy supply. Binarization of DNN has become a promising technology to achieve a high performance with low resource consumption in edge computing. Field-programmable gate array (FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit (CPU) and graphics processing unit (GPU). This paper gives a brief overview of binary neural networks (BNNs) and the corresponding hardware accelerator designs on edge computing environments, and analyzes some significant studies in detail. The performances of some methods are evaluated through the experiment results, and the latest binarization technologies and hardware acceleration methods are tracked. We first give the background of designing BNN and present the typical types of BNNs. The FPGA implementation technologies of BNNs are then reviewed. Detailed comparison with experimental evaluation on typical BNNs and their FPGA implement is further conducted. Finally, certain interesting directions are also illustrated as future work.
A Digital Twin-Based Visual Servoing with Extreme Learning Machine and Differential Evolution

The technology of visual servoing, with the digital twin as its driving force, holds great promise and advantages for enhancing the flexibility and efficiency of smart manufacturing assembly and dispensing applications. The effective deployment of visual servoing is contingent upon the robust and accurate estimation of the vision-motion correlation. Network-based methodologies are frequently employed in visual servoing to approximate the mapping between 2D image feature errors and 3D velocities, offering promising avenues for improving the accuracy and reliability of visual servoing systems. These developments have the potential to fully leverage the capabilities of digital twin technology in the realm of smart manufacturing. However, obtaining sufficient training data for these methods is challenging, and thus improving model generalization to reduce data requirements is imperative. To address this issue, we offer a learning-based approach for estimating Jacobian matrices of visual servoing that organically combines an extreme learning machine (ELM) and a differential evolutionary algorithm (DE). In the first stage, the pseudoinverse of the image Jacobian matrix is approximated using the ELM, which solves the problems associated with traditional visual servoing and is resistant to outside influences such as image noise and mistakes in camera calibration. In the second stage, differential evolution is utilized to select input weights and hidden layer bias and to determine ELM’s output weights. Experimental results conducted on a digital twin operating platform for 4-DOF robot with an eye-in-hand configuration demonstrate better performance than classical visual servoing and traditional ELM-based visual servoing in various cases.