Reviewerly/exHarmony huggingface dataset:
The Optimal Strategy Against the Opponent Adopting Fictitious Play Algorithm in Infinitely Repeated 2 × 2 Games

With the rapid development of Artificial Intelligence(AI), the game between the human and machine/AI becomes common and important. In this paper we will investigate the infinitely repeated games between the human and machine/AI, where the machine/AI adopts the classical learning algorithm, Fictitious Play(FP), to update its actions. We will study the optimal strategy of the human over the infinite time. This problem involves the evolution analysis and optimal control for the game-driven dynamical learning systems, which is different from the traditional control system. We will prove and construct the optimal strategy of the human, which turns out to be dependent on the order relations between the human's payoff values. Further more, under the optimal strategy, the system will enter a cycle if the machine's payoff parameter is rational while the cyclic behavior cannot be maintained if the machine's payoff parameter is irrational. And the period of the cycle or the limiting proportion of outcomes depends on the machine's payoff parameter. In all cases, the FP algorithm can be exploited to the utmost extent, implying the intrinsic drawbacks of the algorithm from this point. Results in this paper are rigorous and might shed some light on the analysis for more general game models and more advanced learning algorithms.
Explainable Natural Disasters Detection Using Transfer Learning

Deep learning applications have far-reaching implications in people's daily lives. Disaster management professionals are becoming increasingly interested in applying deep learning to prepare for and respond to natural disasters. Several deep learning models were used to examine the accuracy and efficiency of the natural disaster image classification in this paper. For image classification, transfer learning models, namely VGGNet19, Inception-v3, ResNet50, and hand-picked architectures from EfficientNet, have been used. As a result, a classification approach based on the ResNet50 architecture is presented. The proposed methodology is expected to produce promising results in classifying natural disaster images, outperforming earlier work with an accuracy score of 95.16%. In addition, explainable artificial intelligence is used to evaluate model performance and visualize the decisions made by showing the edges and parts of images that caused the model to determine a certain label. To meet these requirements, LIME, Grad-CAM, and Grad-CAM++ were used. The algorithms have successfully identified the objects that are relative to the true class label.
Query expansion using Haar wavelet transform

Novice users are unable to express their information needs properly, due to this it is difficult to retrieve all the desired relevant documents from the test collection. The problem of word mismatch is fundamental to information retrieval. Query expansion is a technique in which additional terms are added to retrieve relevant documents. In this article, we have expanded the query using pseudo-relevance feedback and Haar wavelet transform. The performance of the proposed technique is evaluated on FIRE 2011 ad hoc English test Collection and Robust dataset. The mean average precision of the proposed model on the FIRE dataset and Robust Track dataset is 0.3334 and 0.2724, respectively.
Hybrid Movie Recommender System Based on Word Embeddings

A recommender system is an application intended to offer a user item that may be of interest to him according to his profile, the recommendations have been applied successfully in various fields. Recommended items include movies, books, travel and tourism services, friends, research articles, research queries, and much more. Hence the presence of recommender systems in many areas, in particular, movies recommendation. The problem of film recommendation has become more interesting because of the rich data and context available online, what advance quickly the research in this field. Therefore, it’s time to overcome traditional recommendation methods (traditional collaborative filtering, traditional content-based filtering) wich suffer from many drawbacks like cold start problem and data sparsity. In this article we present a solution for these limitations, by proposing a hybrid recommendation framework to improve the quality of online films recommendations services, we used users ratings and movies features, in order to use two models into the framework based on word2vec and Knn algorithms respectively.
Biologically Inspired Variational Auto-Encoders for Adversarial Robustness

Deep Neural Networks (DNNs) have recently become the standard tools for solving problems that can be prohibitive for human or statistical criteria, such as classification problems. Nevertheless, DNNs have been vulnerable to small adversarial perturbations that cause misclassification of legitimate images. Adversarial attacks show a security risk to deployed DNNs and indicate a divergence between how DNNs and humans perform classification. It has been illustrated that sleep improves knowledge generalization and improves robustness against noise in animals and humans. This paper proposes a defense algorithm that uses a biologically inspired sleep phase in a Variational Auto-Encoder (Defense–VAE–Sleep) to purge adversarial perturbations from contaminated images. We are demonstrating the benefit of sleep in improving the generalization performance of the traditional VAE when the testing data differ in specific ways even by a small amount from the training data. We conduct extensive experiments, including comparisons with the state–of–the–art on three datasets: CelebA, MNIST, and Fashion-MNIST. Overall, our results demonstrate the robustness of our proposed model for defending against adversarial attacks and increasing the classification robustness solutions compared with other models: Defense–VAE and Defense–GAN.
Managing Retention as a Stratagem for Employee Job Satisfaction and Organizational Competitiveness in the ITeS Sector

In this continuously changing contemporary economy, organizations, particularly ITeS, have to be able to antedate technological innovations and compete with other enterprises globally. This need makes important a company's ability to evolve through its employees' learning and continuous development. Securing and retaining skilled employees plays a significant role in this process, as employees' knowledge and skills are essential to an organizations capability to be economically competitive. This is possible only when the employees are satisfied with the work and the organizational strategies adopted to retain them. Therefore, the study focuses on the organizational retention strategies that influence employee job satisfaction. The results indicate that the six constructs have a significant positive association with job satisfaction. Further, the demographic variables have a significant association with the employee retention constructs. The ITeS organizations could use the conclusions to identify the downsides and make necessary improvements in enhancing employee job satisfaction.
Data Access, Control, and Privacy Protection in the VODAN-Africa Architecture

Abstract The Virus Outbreak Data Network (VODAN)-Africa aims to contribute to the publication of Findable Accessible, Interoperable, and Reusable (FAIR) health data under well-defined access conditions. The next step in the VODAN-Africa architecture is to locally deploy the Center for Expanded Data Annotation and Retrieval (CEDAR) and arrange accessibility based on the ‘data visiting’ concept. Locally curated and reposited machine-actionable data can be visited by queries or algorithms, provided that the conditions of access are met. The goal is to enable the multiple (re)use of data with secure access functionality by clinicians (patient care), an idea aligned with the FAIR-based Personal Health Train (PHT) concept. The privacy and security requirements in relation to the FAIR Data Host and the FAIRification workspace (to produce metadata) or dashboard (for the patient) must be clear to design the IT architecture. This article describes a (first) practice, a reference implementation in development, within the VODAN-Africa and Leiden University Medical Center community.
Image Retrieval Technology of Economic Regulations Based on Semantic Segmentation

Content-based image retrieval (CBIR) is an important part of pattern recognition and artificial intelligence. It has broad application prospects in many important fields, such as digital library, medical image analysis, petroleum geological survey, and public security information retrieval. In this study, statistical modeling and discriminant learning methods are used to analyze and study some key problems in image retrieval, including image concept retrieval, image example retrieval, and relevance feedback. The main research results obtained are as follows: an image classification method based on the Gaussian mixture model (GMM) and max-min posterior pseudo-probability (MMP) discriminant learning is proposed, which is called GMM-MMP method for short; a concept retrieval method based on GMM-MMP is proposed. According to the image concept, the image is divided into two categories: the concept-related image and the concept-unrelated image. The Gaussian mixture model is used to establish the mapping from the image low-level features to the image concept, and the image is classified according to the posterior pseudo-probability classifier to realize the image concept retrieval; an example retrieval method based on GMM-MMP is proposed. According to the image similarity semantics, the image is divided into two categories: the related image and the uncorrelated image of the example image. The Gaussian mixture model is used to establish the mapping from the low-level features of the image to the image similarity semantics. The image is classified according to the posterior pseudo-probability classifier to realize the image case retrieval. Based on the above work, this study implements a content-based image retrieval system.
Optical measurement of an impact reaction in a modeled wing

In this research, an impact reaction which was provoked far from its origins was studied. A metal box filled with sand was used to emulate a rigid body in which a steel bar was embedded; these conditions simulated the fuselage and wing, respectively, and the impact was applied to the rigid body and the measurement to the bar. For this, an optical technique was used to measure the relative displacement of the steel bar, and the measurements are obtained by applying digital image correlation; 2D images were obtained from the speckles generated as a reflection of the beam on the material. The results were studied through the modified Gauss–Newton analytical approach obtaining a maximum standard error deviation of 0.144 from the experimental results.
Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-Inspired Evolutionary Algorithm in the Game of <i>Carcassonne</i>

Monte Carlo tree search (MCTS) is a sampling best-first method to search for optimal decisions. One popular selection mechanism that has proved to be reliable in MCTS is based on the Upper Confidence bounds for Trees (UCT). This attempts to balance exploration and exploitation. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT formula and use the evolved expressions in MCTS. Specifically, we evolve expressions using our proposed semantic-inspired evolutionary algorithm in MCTS (SIEA-MCTS). This is inspired by semantics in Genetic Programming (GP), where the use of fitness cases is seen as a requirement to be adopted in GP. Fitness cases are normally used to determine the fitness of individuals and can be used to compute the semantic similarity (or dissimilarity) of individuals. However, fitness cases are not available in MCTS. We extend this notion by using multiple reward values from MCTS that allow us to determine both the fitness values of individuals and their semantics. We show how SIEA-MCTS is able to successfully evolve expressions that yield better or competitive results compared to UCT. We compare the performance of the proposed SIEA-MCTS against MCTS algorithms, MCTS rapid action value estimation algorithms, three variants of the *-minimax family of algorithms, a random controller, and two more EA approaches. We consistently show how SIEA-MCTS outperforms most of these intelligent controllers in the game of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Carcassonne</i> .
Adaptive Dark Channel Prior Enhancement Algorithm for Different Source Night Vision Halation Images

The existing enhancement algorithms amplify the halation area and noise when enhancing the night vision halation image.Therefore, this paper proposes an adaptive dark channel prior (ADCP) enhancement algorithm for the different source night vision halation image.The algorithm constructs an adaptive transmittance function according to the relationship between the initial transmittance and the critical gray value of halation.The function can automatically adjust the transmittance according to the halation degree in the night vision image, which ensure the ADCP algorithm to achieve the adaptive enhancement of the images.The experimental results show that the proposed algorithm can effectively improve the clarity and contrast of visible and infrared images in night vision, and avoid over-enhancement of the halation region of visible images.When the proposed algorithm is applied to the anti-halation processing of different source night vision image fusion, the halation elimination of the fused image is more complete, the details of the dark area such as edge, brightness and color are moderately improved, and the overall visual effect is better than the existing enhancement algorithms.The effectiveness and universality of the proposed algorithm are verified for processing different night vision halation scene images.
Left and Right Ventricular Segmentation Based on 3D Region-Aware U-Net

The cardiac is one of the essential organs, and the segmentation of the left and right ventricular of cardiac is essential in diagnosing various heart diseases. The most popular method for the segmentation of 3D MRI images is the nnUNet. However, the 3D MRI volume of the ventricular contains other organs which interfere with the segmentation of the ventricular. Hence, we proposed a novel region-aware U-Net segmentation method RegUNet for ventricular segmentation. RegUNet improves the ventricular's segmentation performance by first capturing the region of interest (RoI) of the ventricular and then segmenting the ventricular with the captured RoI features, which reduces the segmentation module's difficulty by keeping the cardiac's features and leaving others such that RegUNet can focus on ventricular segmentation. Besides, since the model segments the ventricular with the captured RoI features, it saves the model's computing resources from identifying the background of the volume. Since 3D cardiac MRI volumes scanned by the different devices have diverse statistical characteristics, which causes the model's performance in processing the multi-source cardiac volumes to be unstable. We stabilize the model's performance with a multi-sources feature normalization strategy, which normalizes the feature from a different source with different parameters. We validated the proposed method on the M&MS dataset, a multi-sources 3D MRI cardiac segmentation dataset. Experiments showed that RegUNet's segmentation ability reached the state-of-the-art.
Integrating Residual, Dense, and Inception Blocks into the nnUNet

The nnUNet is a fully automated and generalisable framework which automatically configures the full training pipeline for the segmentation task it is applied on, while taking into account dataset properties and hardware constraints. It utilises a basic UNet type architecture which is self-configuring in terms of topology. In this work, we propose to extend the nnUNet by integrating mechanisms from more advanced UNet variations such as the residual, dense, and inception blocks, resulting in three new nnUNet variations, namely the Residual-nnUNet, Dense-nnUNet, and Inception-nnUNet. We have evaluated the segmentation performance on eight datasets consisting of 20 target anatomical structures. Our results demonstrate that altering network architecture may lead to performance gains, but the extent of gains and the optimally chosen nnUNet variation is dataset dependent.
Explanations of Deep Networks on EEG Data via Interpretable Approaches

Despite achieving success in many domains, deep learning models remain mostly black boxes. However, understanding the reasons behind predictions is quite important in assessing trust, which is fundamental in the EEG analysis task. In this work, we propose to use two representative explanation approaches, including LIME and Grad-CAM, to explain the predictions of a simple convolutional neural network on an EEG-based emotional brain-computer interface. Our results demonstrate the interpretability approaches provide the understanding of which features better discriminate the target emotions and provide insights into the neural processes involved in the model learned behaviors.
Practical generic programming over a universe of native datatypes

Datatype-generic programming makes it possible to define a construction once and apply it to a large class of datatypes. It is often used to avoid code duplication in languages that encourage the definition of custom datatypes, in particular state-of-the-art dependently typed languages where one can have many variants of the same datatype with different type-level invariants. In addition to giving access to familiar programming constructions for free, datatype-generic programming in the dependently typed setting also allows for the construction of generic proofs. However, the current interfaces available for this purpose are needlessly hard to use or are limited in the range of datatypes they handle. In this paper, we describe the design of a library for safe and user-friendly datatype-generic programming in the Agda language. Generic constructions in our library are regular Agda functions over a broad universe of datatypes, yet they can be specialized to native Agda datatypes with a simple one-liner. Furthermore, we provide building blocks so that library designers can too define their own datatype-generic constructions.
An Empirical Study of Fully Black-Box and Universal Adversarial Attack for SAR Target Recognition

It has been demonstrated that deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) techniques are extremely susceptible to adversarial intrusions, that is, malicious SAR images including deliberately generated perturbations that are imperceptible to the human eye but can deflect DNN inference. Attack algorithms in previous studies are based on direct access to a ATR model such as gradients or training data to generate adversarial examples for a target SAR image, which is against the non-cooperative nature of ATR applications. In this article, we establish a fully black-box universal attack (FBUA) framework to craft one single universal adversarial perturbation (UAP) against a wide range of DNN architectures as well as a large fraction of target images. It is of both high practical relevance for an attacker and a risk for ATR systems that the UAP can be designed by an FBUA in advance and without any access to the victim DNN. The proposed FBUA can be decomposed to three main phases: (1) SAR images simulation, (2) substitute model training, and (3) UAP generation. Comprehensive evaluations on the MSTAR and SARSIM datasets demonstrate the efficacy of the FBUA, i.e., can achieve an average fooling ratio of 64.6% on eight cutting-edge DNNs (when the magnitude of the UAP is set to 16/255). Furthermore, we empirically find that the black-box UAP mainly functions by activating spurious features which can effectively couple with clean features to force the ATR models to concentrate on several categories and exhibit a class-wise vulnerability. The proposed FBUA aligns with the non-cooperative nature and reveals the access-free adversarial vulnerability of DNN-based SAR ATR techniques, providing a foundation for future defense against black-box threats.
A Big Data Security Architecture Based on Blockchain and Trusted Data Cloud Center

In view of the shortcomings of big data security and privacy protection in cloud environment, a big data security architecture was proposed in this paper. Based on blockchain technology and trusted data cloud center, data security architecture adopts the ideas of trusted authentication, intrusion detection, data segmentation, and decentralized storage and applies Amazon AWS log processing service, PairHand user authentication protocol, and Hadoop data analysis framework to realize dig data security and privacy protection in the cloud environment. This paper realizes system initialization and user authentication, hierarchical data storage, decentralized storage, and user security access. The experimental results show that the system architecture can ensure data security and data access speed, which can provide necessary reference for cloud security.
Application of Moving Target Information Perception Technology in Intelligent Supervision System

In order to solve the problems of inaccurate information collection, incomplete information collection, and inconsistency of collected images in traditional sports injury collection methods, an application method of moving target information perception technology in intelligent supervision system is proposed. By judging and analyzing the potential motion damage posture of the motion posture intelligent tracking images, the collected motion intelligence tracking images are judged. The intelligent tracking image matrix can make up for the shortcomings of traditional images that are not connected, complete the identification, detect potential damage in time, and take targeted preventive measures and means. Finally, according to the target detection algorithm and target tracking algorithm, combined with OpenCV computer vision library and QT image library, an intelligent video surveillance target tracking simulation system is developed. The algorithm studied in this paper is to realize the target tracking function of the intelligent video surveillance system. Through the comparison of experimental results, the design method can accurately collect damage attitude information, without calculating continuous values, and the use of three-dimensional images in the positioning process can analyze the damage attitude from multiple angles.
Enhanced Memetic Algorithm-Based Extreme Learning Machine Model for Smart Grid Stability Prediction

The smart grid is considered a conventional application domain of cyber-physical system (CPS) tools in the electrical utility industry. The physical system dynamics of SG with the assistance of CPS are generally controlled by connected sensors and controllers via a communication link. These CPSs, which rely heavily on an expansive communication network and intelligent computing algorithms, are susceptible to cyber-physical attacks and are also sensitive to various technical, economical, and social factors compromising their stability. Assessment and prediction of the stability of CPSs are very vital in this context. In this work, a novel optimized (memetic algorithm-based) extreme learning machine model for smart grid-CPS stability prediction has been proposed. Here, the teaching-learning-based optimization and simulated annealing techniques are used to design the memetic algorithm. The experimental result regarding the proposed model is then compared with other contemporary machine learning and deep learning models.