Reviewerly/exHarmony huggingface dataset:
Improved Particle Swarm Optimization on Based Quantum Behaved Framework for Big Data Optimization

In recent times, big data has become an essential concern with the rapid increase of digitalization. The problems that find solutions to the problems of finding and evaluating the features of big data are called optimization problems. In this paper, data sets containing EEG signals have been studied. The goal is to detect actual EEG signals while eliminating additional brain activity patterns in the collected data, resulting in more accurate interpretation. In the study, to handle big data optimization (BigOpt) difficulties, a novel swarm intelligence-based technique is developed. A Developed PSO-Q was proposed by updating the random walking phase of the Particle Swarm Optimization on the combined quantum behaved method (PSO-Q) for BigOpt problems. PSO-Q's local search capability has been improved. The success of PSO-Q and IPSO-Q has been thoroughly tested in various cycles (maximum iterations) (300, 400, 500, and 1000) and population sizes (10, 25, and 50) on six data sets. The outcomes of the PSO-Q and IPSO-Q were statistically evaluated with the Wilcoxon Signed-Rank Test. PSO-Q and IPSO-Q have been compared with newly developed swarm-based algorithms (BA, Jaya, AOA, etc.) in the literature in recent years. The success of IPSO-Q has been shown by evaluating the results obtained. The results showed that IPSO-Q can be used as an alternative algorithm in BigOpt problems.
Dynamic Analysis of Deep Integration of Artificial Intelligence Based on High-Performance Computing for Ideological and Political Teaching Evaluation

At present, the teaching evaluation of courses is more and more important, and teaching evaluation is a main means to measure the quality of teachers’ teaching. Among many course teaching evaluations, ideological and political courses have become the main position for teaching evaluation reform due to their unique strategic position and subject specificity. In the era of rapid development of science and technology, artificial intelligence combined with high-performance computing has made frequent progress in various fields. The trend of development is unstoppable. Therefore, based on the current problems in the evaluation of ideological and political teaching, this paper proposes an artificial intelligence theory based on high-performance computing. This paper will closely integrate it with the evaluation of ideological and political teaching. In the research process of this paper, high-performance computing, artificial intelligence, and ideological and political teaching evaluation are explained in detail. This paper focuses on the introduction of artificial intelligence algorithms for high-performance computing. Finally, this paper proves the feasibility of this method in the evaluation of ideological and political teaching through the actual integration experiment with the evaluation of ideological and political teaching and proposes a series of teaching evaluation methods. Finally, the experiment shows that our new evaluation method can pay attention to 56.7% of students’ comprehensive development and 63.7% of students’ personal quality.
A new classifier for imbalanced data with iterative learning process and ensemble operating process

At present, existing methods carry out imbalanced classification at two levels, namely a sampling level and an algorithmic level. However, different sampling methods provide different data distributions for classifiers, which may lead to unreliable decision-making. The algorithmic level methods are often limited to a given classifier. In this paper, we propose a new iterative ensemble classifier for imbalanced data with iterative learning process and ensemble operating process (C-ILEO). There is an ensemble base classifier in each iteration of learning and its elementary classifier learns datasets in a low dimensional space composed of a small number of features which are selected with a new rule based on mutual information. Samples classified into negative class by the ensemble base classifier at each iteration are removed because these samples can no longer provide significant information for the following learning. In addition, the class weight of each elementary classifier is optimized with a new strategy which is similar to the half-interval search algorithm. The ensemble operating process is carried out on what all ensemble base classifiers learned on all the training samples including removed samples during the iteration. Compared with 16 commonly used imbalanced learning methods on 15 real world imbalanced datasets, C-ILEO outperforms most algorithmic, data level and ensemble approaches on metrics of Gmean, F1, Acc, and Precision.
The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning

With recent technological advances in wireless networks and the Internet, social media has become a vital part of the daily lives of people. Social media like Twitter, Facebook, and Instagram have enabled people to instantly share their thoughts and ideas about a particular topic or person's life. Emotion classification in Twitter data remains a hot search topic in the field of artificial intelligence (AI). Though several models have been developed for tweet data in English, it is still needed to develop an effective tweet emotion classification for the Japanese language. In this aspect, this work develops a new artificial intelligence with an Optimal Long Short-Term Memory-Based Japanese Tweet Emotion Classification (OLSTM-JTCC) model in wireless networks. The proposed OLSTM-JTCC technique aims to examine emotions and categorises them into proper class labels. The proposed OLSTM-JTCC technique initially employs the TF-IFD model for the extraction of feature vectors. Besides, the OLSTM model is used to classify the tweet data into different types of emotions that exist within it. In order to improve the classification capability of the LSTM model, the Henry gas solubility optimization (HSGO) algorithm is applied as a hyperparameter optimizer. The performance validation of the OLSTM-JTCC technique took place using Japanese tweets, and the comparative results highlighted the better performance of the OLSTM-JTCC technique in terms of different measures.
An <scp>ECG</scp> denoising method combining variational modal decomposition and wavelet soft threshold

Abstract Cardio‐cerebrovascular disease is one of the malignant diseases with the highest morbidity and mortality in the world. Medical research shows that the factors that induce heart blood cerebrovascular disease are diverse, and electrocardiogram (ECG) has important reference significance for the diagnosis and pathological research of cardiovascular and cerebrovascular diseases. This paper proposes a method of denoising ECG signals by combining variational mode decomposition with wavelet soft‐threshold. First perform variational modal decomposition on the ECG signal to obtain several modal components, and then the decomposed modals components are denoised by wavelet threshold, and finally the signal reconstruction of the denoised modal components is carried out through Matlab simulation experiment analysis. Compared with the soft‐threshold denoising method of ensemble empirical mode decomposition (EEMD), the soft‐threshold denoising method of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the variational modal decomposition wavelet threshold denoising algorithm combined with ECG signal denoising has better denoising effect. This method is used to denoise the ECG signal from the MIT‐BIH database of the Massachusetts Institute of Technology. Under the condition of ensuring the smoothness of the ECG signal image, the signal‐to‐noise ratio value is 27.1121 and the mean square error value is 0.0535, which has achieved good results.
Aircraft Detection in Remote Sensing Imagery Based on Improved YOLOv4

Abstract A method is provided to resolve the issues of false recognition and lower recall of the you only look once version 4 (YOLOv4) algorithm in the aircraft targets prediction of remote sensing imagery. The method is based on the YOLOv4. According to the characteristics of small size and intensive arrangement of aircraft targets in remote sensing images, the backbone is mended to boost the prediction capacity of smaller targets. To strengthen the transmission and reuse of network features, the connection mode of the residual module in YOLOv4 is changed to the dense connection mode, and the five residual blocks in the backbone network are modified to the corresponding dense connection blocks to reduce the network layers and model redundancy. The experiment results reveal that compared with the YOLOv4, our method can enlarge targets features in smaller image regions, thereby availably increasing the prediction precision of aircraft targets.
CoopMC: Algorithm-Architecture Co-Optimization for Markov Chain Monte Carlo Accelerators

Bayesian machine learning is useful for applications that may make high-risk decisions with limited, noisy, or unlabeled data, as it provides great data efficiency and uncertainty estimation. Building on previous efforts, this work presents CoopMC, an algorithm-architecture co-optimization for developing more efficient MCMC-based Bayesian inference accelerators. CoopMC utilizes dynamic normalization (DyNorm), LUT-based exponential kernels (TableExp), and log-domain kernel fusion (LogFusion) to reduce computational precision and shrink ALU area by 7.5&#x00D7; without noticeable reduction in model performance. Also, a Tree-based Gibbs sampler (TreeSampler) improves hardware runtime from $\mathcal{O}$(N) to $\mathcal{O}$(log(N)), an 8.7&#x00D7; speedup, and yields 1.9&#x00D7; better area efficiency than the existing state-of-the-art Gibbs sampling architecture. These methods have been tested on 10 diverse workloads using 3 different types of Bayesian models, demonstrating applicability to many Bayesian algorithms. In an end-to-end case study, these optimizations achieve a 33% logic area reduction, 62% power reduction, and 1.53&#x00D7; speedup over previous state-of-the-art end-to-end MCMC accelerators.
A new class of generalized almost perfect nonlinear power function

As a generalization of almost perfect nonlinear functions, generalized almost perfect nonlinear (GAPN) functions defined over finite fields of odd characteristic have also important applications in cryptography and finite geometry. In this paper, we mainly focus on GAPN power functions, and a new class of such function (up to EA equivalence) is obtained. Furthermore, using our method, almost all the known GAPN power functions can be confirmed in a simple way.
A Better Mechanistic Understanding of Big Data through an Order Search Using Causal Bayesian Networks

Every year, biomedical data is increasing at an alarming rate and is being collected from many different sources, such as hospitals (clinical Big Data), laboratories (genomic and proteomic Big Data), and the internet (online Big Data). This article presents and evaluates a practical causal discovery algorithm that uses modern statistical, machine learning, and informatics approaches that have been used in the learning of causal relationships from biomedical Big Data, which in turn integrates clinical, omics (genomic and proteomic), and environmental aspects. The learning of causal relationships from data using graphical models does not address the hidden (unknown or not measured) mechanisms that are inherent to most measurements and analyses. Also, many algorithms lack a practical usage since they do not incorporate current mechanistic knowledge. This paper proposes a practical causal discovery algorithm using causal Bayesian networks to gain a better understanding of the underlying mechanistic process that generated the data. The algorithm utilizes model averaging techniques such as searching through a relative order (e.g., if gene A is regulating gene B, then we can say that gene A is of a higher order than gene B) and incorporates relevant prior mechanistic knowledge to guide the Markov chain Monte Carlo search through the order. The algorithm was evaluated by testing its performance on datasets generated from the ALARM causal Bayesian network. Out of the 37 variables in the ALARM causal Bayesian network, two sets of nine were chosen and the observations for those variables were provided to the algorithm. The performance of the algorithm was evaluated by comparing its prediction with the generating causal mechanism. The 28 variables that were not in use are referred to as hidden variables and they allowed for the evaluation of the algorithm’s ability to predict hidden confounded causal relationships. The algorithm’s predicted performance was also compared with other causal discovery algorithms. The results show that incorporating order information provides a better mechanistic understanding even when hidden confounded causes are present. The prior mechanistic knowledge incorporated in the Markov chain Monte Carlo search led to the better discovery of causal relationships when hidden variables were involved in generating the simulated data.
Testing Feedforward Neural Networks Training Programs

At present, we are witnessing an increasing effort to improve the performance and trustworthiness of Deep Neural Networks (DNNs), with the aim to enable their adoption in safety critical systems such as self-driving cars or aircraft collision-avoidance systems. Multiple testing techniques are proposed to generate test cases that can expose inconsistencies in the behavior of DNN models. These techniques assume implicitly that the training program is bug-free and appropriately configured. However, satisfying this assumption for a novel problem requires significant engineering work to prepare the data, design the DNN, implement the training program, and tune the hyperparameters to produce the model for which current automated test data generators search for corner-case behaviors. All these model training steps can be error prone. Therefore, it is crucial to detect and correct errors throughout all the engineering steps of DNN-based software systems and not only on the resulting DNN model. In this article, we gather a catalog of training issues and based on their symptoms and their effects on the behavior of the training program, we propose practical verification routines to detect the aforementioned issues, automatically, by continuously validating that some important properties of the learning dynamics hold during the training. Then, we design TheDeepChecker , an end-to-end property-based debugging approach for DNN training programs and implement it as a TensorFlow-based library. As an empirical evaluation, we conduct a case study to assess the effectiveness of TheDeepChecker on synthetic and real-world buggy DL programs and compare its performance to that of the Amazon SageMaker Debugger ( SMD ). Results show that TheDeepChecker ’s on-execution validation of DNN-based program’s properties through three sequential phases (pre-, on-, and post-fitting) succeeds in revealing several coding bugs and system misconfigurations errors early on and at a low cost. Moreover, our property-based approach outperforms the SMD ’s offline rules verification on training logs in terms of detection accuracy for unstable learning issues and coverage of additional DL bugs.
A novel MCDM ensemble approach of designing an ELM based predictor for stock index price forecasting

In support of its faster learning capacity and better generalization, Extreme Learning Machine (ELM) has gained the attention of researchers as a means to solve various real-world prediction problems. However, the performance of ELM is heavily dependent on the activation functions used in it. In this study, design of ELM is addressed as a Multi Criteria Decision Making (MCDM) problem. The selection of the activation function for the ELM based predictor model is done through a novel MCDM ensemble approach. On the basis of 9 prediction metrics, MCDM techniques such as TOPSIS, PROMETHEE-II, and VIKOR were used to assess and rank 15 activation functions on ELM performance. In light of the fact that the ranks determined by each MCDM technique do not coincide, a novel ensemble approach was proposed to calculate the final rank score by considering the occurrences of each model in the primary ranking and its respective rank score. In the end, the most highly ranked activation function is taken into account in the ELM-based predictor model. The proposed model is assessed over three benchmark stock indices such as BSE SENSEX, NIFTY 50 and BSE S&amp;P 500. The empirical analysis clearly shows that the ELM based predictor model designed using ELU activation function performs competitively compared to other reported models.
Defining Frames to Structure Agile Development in Hybrid Settings - A Multi-Case Interview Study

Companies often combine agile and plan-based methods to so-called hybrid development approaches to benefit from the advantages of both. Recent research highlights conflicts introduced when combining agile and plan-based approaches in the different phases of the software lifecycle. For example, using both agile and plan-based methods during the requirements engineering of a project requires a decision on how many requirements should be gathered up-front and how many can be gathered during the runtime of a project. These conflicts need to be solved in order to construct a successful development approach. In order to investigate why the conflicts exist, how they are addressed in industry, and how they are related to each other, we performed a multi-case interview study with 15 practitioners. Our results reveal that the conflicts exist because companies use plan-based approaches to structure their agile development and define spaces of freedom and flexibility at the same time. From this insight and our results, we derive a theory that shows how companies structure their development stepwise by defining frames.
The Optimization of Hospital Financial Management Based on Cloud Technology and Wireless Network Technology in the Context of Artificial Intelligence

In the reign of Internet technology, cloud technology has emerged as one of the powerful tools in computing with many advantages in the bag. Cloud technology is defined as online access to all types of computing applications. The applications range from access to data, servers, software, databases to storage and retrieval of data. The data can be accessed from anywhere, anytime. The user does not need to own the hardware in this case. Clouds have multiple data centers located at various places. On-demand data service is the best part of cloud technology. This technology is made use in many industries. In this research, we will study how cloud technology and wireless networking technology are applied to optimize hospital financial management. The healthcare industry faces various challenges in financial management such as budgeting, growth planning, and cost-effectiveness. Considering this scenario, it is evident that there is a solid need to optimize its financial management. The cloud computing technology driven by artificial intelligence (AI) is deployed in carrying out repetitive tasks and automation, resulting in increased productivity. In this research, the proposed system uses wireless networking systems and cloud technology to optimize hospital financial management. The model works with the implementation of a hybrid of fuzzy neural networks. The proposed model is analyzed with the existing fuzzy logic and neural network models, and it is found that the proposed model has obtained an accuracy of 97.56% with minimum resources for successful financial management at hospitals.
A Comprehensive Analysis on Face Recognition using various Deep Learning Techniques

Identifying human faces are difficult issue whereas the occurrence of huge diversity in facial pose, lighting, and poor picture quality. Some of the most significant issues with face recognition is object recognition and computer vision. Face recognition still has a lot of issues that need to be worked out and can be resolved using more powerful methods to a larger extent. The application of face recognition in a real-life situation still remains, a challenging task. The most important processes in face recognition, Pre-processing, Face Detection, Feature Extraction, and Classifications remains that should be followed when identifying faces in the wild. The purpose of this paper to assess a variety of deep learning methods; how its work with face recognition for identifying face images in a large database and also explained about various datasets that are used for image classification. To comprehend the present state-of-the-art, of this paper delivers a full review of various DL approaches.
Dynamic Re-Weighting and Cross-Camera Learning for Unsupervised Person Re-Identification

Person Re-Identification (ReID) has witnessed tremendous improvements with the help of deep convolutional neural networks (CNN). Nevertheless, because different fields have their characteristics, most existing methods encounter the problem of poor generalization ability to invisible people. To address this problem, based on the relationship between the temporal and camera position, we propose a robust and effective training strategy named temporal smoothing dynamic re-weighting and cross-camera learning (TSDRC). It uses robust and effective algorithms to transfer valuable knowledge of existing labeled source domains to unlabeled target domains. In the target domain training stage, TSDRC iteratively clusters the samples into several centers and dynamically re-weights unlabeled samples from each center with a temporal smoothing score. Then, cross-camera triplet loss is proposed to fine-tune the source domain model. Particularly, to improve the discernibility of CNN models in the source domain, generally shared person attributes and margin-based softmax loss are adapted to train the source model. In terms of the unlabeled target domain, the samples are clustered into several centers iteratively and the unlabeled samples are dynamically re-weighted from each center. Then, cross-camera triplet loss is proposed to fine-tune the source domain model. Comprehensive experiments on the Market-1501 and DukeMTMC-reID datasets demonstrate that the proposed method vastly improves the performance of unsupervised domain adaptability.
Cyber Security for Secured Smart Home Applications Using Internet of Things, Dark Web, and Blockchain Technology in the Future

The need for more comfortable and humane living spaces has accelerated the development of smart homes. Many extremely intelligent houses are part of the internet of things. It operates with the dark web and ensures the privacy of incognito relays and massive data to properly manage customer requests. This increased demand gives rise to a great deal of concern regarding scalability, efficiency, and safety for a smart home system. Detailed data or the lowest levels that can be in a target set are granular data. In this chapter, the authors present the combination of integrating block-chain technology, dark web, and cloud computing in an effective manner. Blockchain technology is decentralized because it can serve processing services. To ensure the safety of the smart home network, the model employs multivariate correlation analysis and the detection of correlations between traffic functions. The performance of the architecture was evaluated with various performance parameters, and blockchain was found to be an effective security solution for future networks on the internet.