Reviewerly/exHarmony huggingface dataset:
Towards Resilient and Efficient Big Data Storage: Evaluating a SIEM Repository Based on HDFS

Building an efficient, scalable, distributed storage system is challenging in a variety of industries. Currently, the most promising and efficient way to organize this method of data storage is using the Hadoop Distributed File System (HDFS). It is of interest to develop an approach to optimize the distribution of replicas across storage nodes, which allows one to provide the required resilience and efficiency of storing big data in distributed systems based on HDFS. The analysis showed that in the well-known works on processing big data, the issues of simultaneous provision of resilient and efficient data storage are practically not raised. The paper proposes an approach based on the application of the developed probabilistic models for assessing the resilience and efficiency of data storage. The models take into account the random and deterministic modes of distribution of data blocks across the network nodes and allow one to solve the task of providing resilient and efficient big data storage in three kinds of criteria: resilience maximization, efficiency maximization, and restrictions of resilience and efficiency. The objective of the experiment was to optimize the variables of the the security information and event management (SIEM) system. Experiments have confirmed the effectiveness of the proposed approach and the possibility of solving with its help the assigned tasks to satisfy the three selected species. At the same time, genetic algorithms (GAs) were used for the deterministic mode, in which some improvements were introduced regarding the construction of chromosomes and types of fitness functions.
An Efficient and Secure Data Sharing Method Using Asymmetric Pairing with Shorter Ciphertext to Enable Rapid Learning in Healthcare

The recent advent of cloud computing provides a flexible way to effectively share data among multiple users. Cloud computing and cryptographic primitives are changing the way of healthcare unprecedentedly by providing real-time data sharing cost-effectively. Sharing various data items from different users to multiple sets of legitimate subscribers in the cloud environment is a challenging issue. The online electronic healthcare system requires multiple data items to be shared by different users for various purposes. In the present scenario, COVID-19 data is sensitive and must be encrypted to ensure data privacy. Secure sharing of such information is crucial. The standard broadcast encryption system is inefficient for this purpose. Multichannel broadcast encryption is a mechanism that enables secure sharing of different messages to different set of users efficiently. We propose an efficient and secure data sharing method with shorter ciphertext in public key setting using asymmetric (Type-III) pairings. The Type-III setting is the most efficient form among all pairing types regarding operations required and security. The semantic security of this method is proven under decisional BDHE complexity assumption without random oracle model.
Optimization of Business English Teaching Based on the Integration of Interactive Virtual Reality Genetic Algorithm

With the continuous advancement of the global economy and the deepening of internationalization and openness, it is also necessary to keep pace with the times in the optimization of business English teaching. At this point, a genetic algorithm based on the interactive virtual reality (VR) should be established for the optimization of business English teaching to adapt to the future trend in business English teaching optimization more appropriately. In the business English teaching process at present, it is required to change the traditional teaching concept, adjust the previous teaching ideas, widen the horizon continuously, establish an international and diversified English teaching and training program, incorporate the outstanding foreign teaching models, and proactively absorb the excellent educational concepts to drive the development of the domestic business English teaching model more effectively. The results of the simulation experiment indicate that the improved algorithm designed in this article can reduce the computational overhead of the meta-algorithm to a great extent, and the improvement strategy is designed based on the evaluation results of practical examples.
A Survey on Generative Adversarial Networks based Models for Many-to-many Non-parallel Voice Conversion

Voice Conversion (VC) is a task of converting speaker-dependent features of a source speaker&#x0027;s speech without changing the linguistic content. There are many successful VC systems, each trying to overcome some challenges. These challenges include the unavailability of parallel data and solving problems due to the language difference between the source and target speech. Also, one of these challenges is extending the VC system to cover a conversion across many source and target domains with minimal cost. Generative Adversarial Networks (GANs) are showing promising VC results. This work focuses on exploring many-to-many non-parallel GAN-based mono-lingual VC models (nine models that are highly cited), explains the used evaluation methods including objective and subjective methods (eight evaluation methods are presented), and comments on these models.
A novel reputation-based consensus framework (RCF) in distributed ledger technology

Distributed ledger technology (DLT) has emerged as a key and effective technology which has caught the attention of variety of disciplines and is capable of changing lots of sciences and related industries. Selecting the transactions of members in DLT-based technologies such as blockchain, especially in finance, is essential and vital. Therefore, in this paper, we present a novel framework for selecting members' transactions through trust and reputation models called the reputation-based consensus framework (RCF), so that by combining trust and reputation models with blockchain, the target node (or verifier), which has the highest reputation based on its transactions history, is selected to create and publish the current block of blockchain. We have also provided a state machine model to accurately describe the behavior of the RCF. In addition, the RCF algorithms are presented and a prototype is developed based on it. The results of simulation and performance and security analysis proved that RCF leads to fair selection of transactions, improves scalability and throughput, and can resist against attacks such as Sybil and malicious behaviors.
Online Learning of Parameters for Modeling User Preference Based on Bayesian Network

By analyzing users’ behavior data for personalized services, most state-of-the-art methods for user preference modeling are often based on batch-mode machine learning algorithms, where all rating data are assumed to be available throughout the training process. However, data in the real world often arrives sequentially and user preference may change dynamically. The real-time characteristics of rating data make the algorithms for preference modeling challenging to suit real-world online applications. By the user preference model (UPM) based on Bayesian network with a latent variable (BNLV), uncertain relationships among relevant attributes of users, objects and ratings could be represented, in which user preference is represented by the latent variable. In this paper, we propose an online approach for parameter learning of UPM. Specifically, we first extend the classic Voting EM algorithm by using Bayesian estimation in terms of the situation with latent variables. Consequently, we propose the algorithm for learning parameters of UPM from few and sequentially-changing rating data to reflect the gradually changing preferences. Finally, we test the effectiveness of our proposed algorithm by conducting experiments on various datasets. Experimental results demonstrate the superiority of our method in various measurements.
Multiserver configuration for cloud service profit maximization in the presence of soft errors based on grouped grey wolf optimizer

With the growing demand of cloud customers for computing resources, cloud computing has become more and more popular. As a pay-as-you-go model, cloud computing enables customers to use cloud services on demand anytime, anywhere over the Internet and it has become the backbone of modern economy. Obviously, profit maximization is especially important for cloud service providers (CSPs) in a competitive cloud service market. Extensive research papers have been conducted during the past few years for CSPs to optimize cloud service profit, whereas few of them considers the transient faults (resulting in soft errors) that may happen during service requests' execution and thus cause failed execution of these requests. In this paper, we study the multiserver configuration problem for cloud service profit maximization considering the deadline miss rate of service requests and the soft error reliability of the multiserver system. To solve the profit optimization problem, we first construct the models of multiserver system, deadline miss rate, and soft error reliability. Based on these models, we derive the models associated with cloud service revenue and cloud service costs. Then, we formulate the cloud service profit optimization problem and propose an effective grouped grey wolf optimizer (GWO)-based heuristic method that can determine the optimal multiserver configuration for a given customer demand to maximize cloud service profit. Experimental results show that the cloud service profit improvement achieved by our scheme can be up to 33.76% as compared with a state-of-the-art benchmark scheme.
Syntax-Informed Question Answering with Heterogeneous Graph Transformer

Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks. These models are expensive to train. We propose to evaluate whether such pre-trained models can benefit from the addition of explicit linguistics information without requiring retraining from scratch. We present a linguistics-informed question answering approach that extends and fine-tunes a pre-trained transformer-based neural language model with symbolic knowledge encoded with a heterogeneous graph transformer. We illustrate the approach by the addition of syntactic information in the form of dependency and constituency graphic structures connecting tokens and virtual vertices. A comparative empirical performance evaluation with BERT as its baseline and with Stanford Question Answering Dataset demonstrates the competitiveness of the proposed approach. We argue, in conclusion and in the light of further results of preliminary experiments, that the approach is extensible to further linguistics information including semantics and pragmatics.
Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems

The growing complexity of Cyber-Physical Systems (CPS) and challenges in ensuring safety and security have led to the increasing use of deep learning methods for accurate and scalable anomaly detection. However, machine learning (ML) models often suffer from low performance in predicting unexpected data and are vulnerable to accidental or malicious perturbations. Although robustness testing of deep learning models has been extensively explored in applications such as image classification and speech recognition, less attention has been paid to ML-driven safety monitoring in CPS. This paper presents the preliminary results on evaluating the robustness of ML-based anomaly detection methods in safety-critical CPS against two types of accidental and malicious input perturbations, generated using a Gaussian-based noise model and the Fast Gradient Sign Method (FGSM). We test the hypothesis of whether integrating the domain knowledge (e.g., on unsafe system behavior) with the ML models can improve the robustness of anomaly detection without sacrificing accuracy and transparency. Experimental results with two case studies of Artificial Pancreas Systems (APS) for diabetes management show that ML-based safety monitors trained with domain knowledge can reduce on average up to 54.2% of robustness error and keep the average F1 scores high while improving transparency.
Designing Extended Reality Guidance for Physical Caregiving Tasks

In this short paper we explore the opportunities and challenges of designing XR technologies to support the collaborative work between family caregivers and clinicians as they attend to the physical care needs of patients in the home setting.
Diagnosis of Cervical Cancer with Oversampled Unscaled and Scaled Data Using Machine Learning Classifiers

Cervical cancer is a gynecological disease and one of the primary causes of death in females from cancer in developing countries. The recovery rate of this cervix infection can be escalated by premature detection and screening. Automated detection of cancerous cells is proving more efficient and robust in contrast to the medical examination. Machine Learning algorithms are the key to artificial intelligence (AI) in medical images processing. These methods are pretty much helpful in the detection of cancer cells at an initial stage. In this paper, we analyze the performance comparison of eight popular machine learning models as Support Vector Machine (SVM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Random Forest (RF) on original and scaled data to detect the cervical cancer of malignant category. It is obtained from the analysis that the RF classifier performs superbly for the oversampled cervical cancer patient data with a standard scaler.
Chaotic Search-Based Salp Swarm Algorithm for Dealing with System of Nonlinear Equations and Power System Applications

The system of nonlinear equations (SNLEs) is one of the eminent problems in science and engineering, and it is still open to research. A new hybrid intelligent algorithm is presented in this research to solve SNLEs. It is a composite of the salp swarm algorithm (SSA) and chaotic search technique (CST). The proposed methodology is named chaotic salp swarm algorithm (CSSA). CSSA is designed as an optimization process, whereby feasible and infeasible solutions are updated to move closer to the optimum value. The use of this hybrid intelligent methodology aims to improve performance, increase solution versatility, avoid the local optima trap, speed up convergence and optimize the search process. Firstly, SNLEs are transformed into an optimization problem. Secondly, CSSA is used to solve this optimization problem: SSA is used to update the feasible solutions, whereas the infeasible solutions are updated by CST. One of the most significant advantages of the suggested technique is that it does not ignore infeasible solutions that are updated, because these solutions are often extremely near to the optimal solution, resulting in increased search effectiveness and effective exploration and exploitation. The algorithm’s mathematical model is presented in detail. Finally, the proposed approach is assessed with several benchmark problems and real-world applications. Simulation results show that the proposed CSSA is competitive and better in comparison to others, which illustrates the effectiveness of the proposed algorithm. In addition, a statistical analysis by the Wilcoxon rankings test between CSSA and the other comparison methods shows that all p-values are less than 0.05, and CSSA achieves negative ranks’ sum values (R−) much better than the positive ranks’ sum values (R+) in all benchmark problems. In addition, the results have high precision and show good agreement in comparison with similar methods, and they further proved the ability of CSSA to solve real-world applications.
PFF-CB: Multiscale Occlusion Pedestrian Detection Method Based on PFF and CBAM

Occlusion pedestrian detection is an important and difficult task in pedestrian detection. At present, the main method to deal with occlusion pedestrian detection usually adopts pedestrian parts or human body relationship methods. However, in the scene of crowd occlusion or severe pedestrian occlusion, only small parts of the body can be used for detection. Pedestrian parts or human body relationship methods cannot effectively address these issues. In view of the above problems, this paper abandoned the occlusion processing method of pedestrian parts or human body relationship. Considering that it is difficult to establish the relationship between parts and key points. The scale of visible parts of the occlusion pedestrian is small, and the scale of no occlusion pedestrian and occlusion pedestrian in the same picture is different. A multiscale feature attention fusion network named parallel feature fusion with CBAM (PFF-CB) is proposed for occlusion pedestrian detection. Feature information of different scales can be integrated effectively in the PFF-CB module. PFF-CB module uses a convolutional block attention module (CBAM) to enhance the important feature information in space and channel. A parallel feature fusion module based on FPN is used to enhance key features. The performance of the proposed module was tested on two common data sets of occlusion pedestrians with different occlusion types. The results show that the PFF-CB module makes a good performance in occlusion pedestrian detection tasks.
Locally Linear Embedding Based on Sobol Sequence Initialized Black Widow Optimization Algorithm

Locally linear embedding (LLE) algorithm is a nonlinear dimensionality reduction algorithm for manifold learning in the field of artificial intelligence (AI), it can learn locally linear low-dimensional manifolds in high-dimensional space, which means that its core idea is to seek a continuous homeomorphism from high-dimensional space to low-dimensional space, and mainly achieves the purpose of nonlinear dimensionality reduction by keeping the linear algebraic structure of high-dimensional space unchanged in low-dimensional space. The loss function of LLE algorithm is generally solved by gradient descent (GD) method or stochastic gradient descent (SGD) method, but the gradient dependent optimization method is often easy to fall into the trap of local minimum, and as closer it is to the best solution, there is often the sawtooth effect of violent oscillation. In order to overcome the above shortcomings, a Sobol sequence initialized black widow optimization algorithm (SBWOA) is proposed to improve the global optimality, randomness and robustness of the optimization algorithm, and the effectiveness of the algorithm is verified by numerical experiments.
An Exploratory analysis of Machine Learning adaptability in Big Data Analytics Environments: A Data Aggregation in the age of Big Data and the Internet of Things

The paper discusses a new concept combining the potentialities of Big Data processing as well as machine learning developed for security monitoring of mobile Internet of Things. The structure of the security monitoring system is considered as a most effective and useful element to create a new viewpoint of mobile IoT. This article focuses implementation of machine learning in online education. Thus mobile IoT has found successful application in few areas such as security monitoring in public places, transport management, medicine, smart houses, industrial production, electrical consumption, and robotics. All the mathematical foundations along with issues related to this have been considered in this study. In order to solve the classification task, several machine learning mechanisms have been mentioned in this paper. Large organizations are incorporating data-driven actions, and decision making in organizational function. The role of data aggregation is effective here achieving the business objectives. Vast amount of raw data can be processed linearly through data aggregation. This article describes the interaction of data aggregation through wireless networking assuming its effectiveness in online education. Data aggregation in machine learning is highlighted based on evidence based data. The purpose of this research article is to investigate the machine learning adaptability in big data analytics environments with the approach of IoT. In order to collect accurate data, the researcher has taken the help of a secondary data collection method. It has helped the researcher to find out the valid information about mobile IoT. In addition, qualitative methods have been adapted to malaise the collected data within a systematic way. Moreover, this study will help the readers to understand the value of mobile IoT helping in machine learning adaptability in big data analytics.
Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

With the rapid development of Internet technology, new media is more and more favored by people and has become an important medium to control online public opinion. Social public opinion caused by new media is also more and more concerned by all walks of life. New media has the characteristics of fast information dissemination, wide dissemination range, and strong arbitrariness of news release. Positive online public voice or negative online public voice will have a very different impact on people’s lives. Some negative online public voice may even constitute a social crisis and seriously affect social public security. In order to analyze and predict the development trend of new media network public opinion, this paper presents a design of improved BP neural network model based on genetic algorithm, which is used to analyze public opinion in new media network. Experimental results show that this way has stronger processing ability and higher warning accuracy for online public opinion event index data. It can provide certain theoretical basis and data support for relevant departments to effectively prevent and manage new media network public opinion events.
Effects of Body Duplication and Split on Body Schema

Visual-tactile synchronization or visual-motor synchronization makes a not innate body feel as if it was one’s own body (illusory body ownership). Although recent studies have shown that body ownership can be induced in multiple bodies, it is unclear whether the body ownership is switched between bodies, or we feel that multiple bodies belong to us simultaneously. If the body ownership is induced in multiple bodies simultaneously, we expected the body schema to be extended to correspond to the number of bodies. In this study, we investigated whether body ownership is induced in multiple bodies simultaneously by measuring the body schema when the bodies are replicated and split. Experiment 1 investigated whether two side-by-side virtual bodies moved synchronously with the participant’s movements to extend the body schema. In Experiment 2, virtual split bodies in which a virtual body was split into left and right sides were presented. As a result, the body schema was extended only in the split bodies. However, it changed even in the no body ownership condition, suggesting that the appearance of the split bodies simply affected the body schema, not that body ownership of the split bodies occurred at the same time. The results of the questionnaire indicated that the split bodies might be perceived as a single body.