Reviewerly/exHarmony huggingface dataset:
Minimum class variance class-specific extreme learning machine for imbalanced classification

• Handle the imbalanced classification problems. • Minimum class variance class-specific extreme learning machine. • The training time significantly lower than variances-constrained WELM. • Benchmark results confirm effectiveness of proposed classifier. Imbalanced problems occur in real-world applications when the number of majority instances far exceeds the number of minority instances. Traditional extreme learning machine (ELM) classifier becomes biased towards the majority class due to imbalanced learning. To handle this inherent drawback, several modifications of ELM have been proposed such as weighted ELM (WELM), variances-constrained WELM (VW-ELM) to tackle the class imbalance problem effectively. One of our recent works class-specific ELM (CSELM) employs class-specific regularization and has been shown to outperform WELM for imbalanced learning. Motivated by CSELM, this work proposes a minimum class variance class-specific extreme learning machine (MCVCSELM), a variant of CSELM for tackling binary class imbalance problems more effectively. MCVCSELM uses the advantages of both the minimum class variance and the class-specific regularization. The proposed work also has lower computational complexity compared to WELM and VW-ELM. In class-specific cost regulation ELM (CCR-ELM), the calculation of the regularization parameters does not consider class distribution and class overlap. However, the performance of the CCR-ELM is comparable to ELM. MCVCSELM utilizes a class-specific regularization parameter whose value is decided by using the class proportion. The experimental results on 38 binary class datasets with different imbalanced ratios demonstrate that the proposed algorithm outperforms several state-of-the-art methods for imbalanced learning.
Study of Swarm Intelligence Algorithms for Optimizing Deep Neural Network for Bitcoin Prediction

Blockchain, a shared digital ledger, operates on a peer-to-peer network which is used for storing the transactions. Cryptocurrencies are used for transactions in blockchain. The most popular breed among cryptocurrency was bitcoin. Predicting the day-to-day value of bitcoin is a challenging task due to nonlinear and market volatility. There are many statistical methods and machine learning algorithms proposed to forecast the cost of bitcoin, but they were lacking to predict the correct result when the input data set is larger and has more noise. To handle large data set, a deep learning technique has been used. The deep learning algorithms, especially LSTM network, also have some drawbacks such as high computational time, inability to generate higher quality prediction result. To avoid these shortcomings and make LSTM a better model for bitcoin prediction, it is necessary to optimize LSTM network. This paper presents a comparative study of numerous optimized deep learning techniques to forecast the price of bitcoin.
Cyclic Remote Implementation of Partially Unknown Quantum Operations

We present a novel scheme of Cyclic remote implementation of partially unknown quantum operations (CRIPUQO) via a six-qubit entangled state as the quantum channel. Alice can remotely apply a partially unknown quantum operation Ud (d = 0,1) to Bob's qubit b, and Bob can remotely apply Ud to Charlie's site; meanwhile, Charlie can also remotely apply Ud to Alice's qubit c. It is pointed out that the CRIPUQO in the opposite direction can be perfectly achieved by changing the quantum channel. The scheme above is also generalized to systems having N observers, so that the CRIPUQO can be realized in quantum information networks with N observers in different directions by changing quantum channels.
Time-dependent switching of the photon entanglement type using a driven quantum emitter–cavity system

The cascaded decay in a four-level quantum emitter is a well established mechanism to generate polarization entangled photon pairs, the building blocks of many applications in quantum technologies. The four most prominent maximally entangled photon pair states are the Bell states. In a typical experiment based on an undriven emitter only one type of Bell state entanglement can be observed in a given polarization basis. Other types of Bell state entanglement in the same basis can be created by continuously driving the system by an external laser. In this work we propose a protocol for time-dependent entanglement switching in a four-level quantum emitter--cavity system that can be operated by changing the external driving strength. By selecting different two-photon resonances between the laser-dressed states, we can actively switch back and forth between the different types of Bell state entanglement in the same basis as well as between entangled and nonentangled photon pairs. This remarkable feature demonstrates the possibility to achieve a controlled, time-dependent manipulation of the entanglement type that could be used in many innovative applications.
Improving Impulse Noise Classification Using Ensemble Learning Methods

Medical image denoising is an essential pre-processing step in medical image processing which improves the performance of clinical diagnosis and prognosis. The high level medical image processing algorithms like segmentation, classification etc. works better if the image is denoised appropriately. The main objective of this research work is to find and replace only the corrupted pixels with suitable estimates of pixels in medical images. The other pixels which are not corrupted are left undisturbed, thereby preserving the image quality for proper diagnosis. For the primary task of finding the corrupted pixels, an ensemble of machine learning (EML) classifiers namely Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT or RT) and Random Forest (RF) are used by supervised learning methods. The final classification output is determined by the majority voting of the outputs of each ML classifier which works in parallel. By adopting this method, a classification accuracy of 99.87% is achieved.
Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis

We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.
Water Quality Prediction Method Based on Transfer Learning and Echo State Network

Due to the adjacency effect of space, the water quality information of adjacent monitoring points in the same water area is relevant. However, traditional prediction methods use the historical water quality information of only one monitoring point rather than other nearby monitoring points. It is important to note that the sample quantity has a great influence on the prediction accuracy of the prediction methods which require lots of training samples. In addition, the water quality information is time series. Whether the time sequence of water quality information can be effectively used has direct impact on the prediction accuracy. Therefore, this paper proposes a water quality prediction method based on transfer learning and echo state network (ESN). First, we transfer the features of water quality samples of the nearby monitoring point to the prediction monitoring point based on transfer component analysis, and further select the high-quality samples based on similarity and time sequence. In this way the sample quantity of the prediction monitoring point is increased on the premise of ensuring the sample quality. Second, we construct a water quality prediction model based on ESN which takes the advantage of the time sequence of water quality information, improving the prediction accuracy on the premise of ensuring the training time. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experiment results demonstrate that the proposed method can expand the quantity of training samples and further improve the accuracy of water quality prediction by making full use of the space-time characteristics of water quality information.
Bayesian subgroup analysis in regression using mixture models

Heterogeneity occurs in many regression problems, where members from different latent subgroups respond differently to the covariates of interest (e.g., treatments) even after adjusting for other covariates. A Bayesian model called the mixture of finite mixtures (MFM) can be used to identify these subgroups, a key feature of which is that the number of subgroups is modeled as a random variable and its distribution is learned from the data. The Bayesian MFM model was not commonly used in earlier applications largely due to computational difficulties. In comparison, an alternative infinite mixture model called the Dirichlet Process Mixture (DPM) model has been a main Bayesian tool for clustering even though it is a mis-specified model for many applications. The popularity of DPM is partly due to its convenient mathematical properties that enable efficient computing algorithms. A class of Bayesian models tailored to regression problems, the conditional MFMs (cMFM), are described and studied. Computing for the cMFM is developed by extending the efficient MCMC algorithms for general MFMs. Using simulation and real data examples, the cMFM is compared to existing frequentist methods, the conditional DPM, and the original MFM and DPM models that model response and covariates jointly. The cMFM is shown to be favorable in clustering accuracy and is robust to different covariates and noise distributions.
An Autonomous Cleaning Robot

An autonomous cleaning robot has to balance between good quality cleanliness and reducing the wastage of time. Even, having the robot with human control will cost our time. The present work is designed to solve this problem. A room is considered with and without obstacles and simulation of an autonomous robot to do the cleaning is performed for various scenarios. An efficient energy saving algorithm for path planning is proposed and simulation of the robot performing the task in the shortest distance is accomplished. So, a best path is proposed for given scenarios such that the robot travels minimum distance thereby increasing efficiency.
Handling of Line Breaking on Latin-to-Balinese Script Transliteration Web Application as Part of Balinese Language Ubiquitous Learning

This study is aimed to preserve part of Balinese local culture that is endangered, i.e. Latin-to-Balinese Script transliteration knowledge through the web technology that support ubiquitous learning. It analyzed handling of line breaking on Latin-to-Balinese Script transliteration web application since there has not been studied yet on no-word boundaries (scriptio continua) script. There are two rules of thumb should be applied: 1) No line breaking is allowed between syllable-sign cluster or consonant-vowel cluster; and 2) No line breaking is allowed just before a colon, comma, or period. This research added certain perspective and strengthened the transliteration knowledge, as part Balinese Language ubiquitous learning that support Balinese Language education, which is a mandatory local subject from basic to high school in Bali Province. This analysis was conducted on Hanacaraka web application that was developed as a technological product of Universitas Pendidikan Ganesha (Undiksha), Indonesia. Hanacaraka basically receives Latin text input and outputs Balinese Script based on Bali Simbar font. Through the experiment, its handling on line breaking gave good transliteration result since special algorithm was applied. Future work to enrich and strengthen the transliteration knowledge is by extending the handling of line breaking on Latin-to-Balinese Script web application that was supported by dedicated Balinese-Unicode font.
ServQual and WebQual 4.0 for usability check academic information system of private university

Abstract This study aims to determine the level, correlation, and influence of the usability of academic information system services using the ServQual and WebQual 4.0 methods. The research method used is qualitative-qualitative and data collection through a survey verification consisting of 20 statements. The questionnaire statement uses a Likert scale with 100 student respondents as the main users. The sampling technique uses a simple random sampling technique. Then, the data analysis in this study uses path analysis. The results showed that ServQual was at an average score of 2.60, WebQual 4.0 was at an average score of 2.59, and the average score for the User Satisfaction variable was at 2.66. Academic information system improvement will have a major effect on User Satisfaction. This is by the results of the verification analysis which shows that ServQual (X1) and WebQual 4.0 (X2) have a positive effect simultaneously on User Satisfaction (Y), with a total effect of 0.688 or 68.8%. External influence of 0.312 or 31.2%. Thus, the academic information system in small and medium-scale private universities needs to be improved on the WebQual 4.0 variable and can be added by ServQual improvements, and ServQual improvements can also be done without including WebQual 4.0.
Big Social Data and Modelling Frameworks

Social media is a platform that provides information about every aspect of a person’s life. By checking up on someone’s social media handle, users can learn about their emotions, feelings, and mood. As a result, in this work, researchers can analyze social media data and model people’s emotions and moods. There are two main parts in this framework. The first ensemble classifier scheme is a machine learning tool used to recognize emotional content in data uploaded by users to social media. The second method is a graph-based in which emotional topics are plotted on a graph against people’s emotions based on data obtained from a social media platform.
A multi-facets ontology matching approach for generating PLC domain knowledge graphs

Programmable Logic Controller (PLC) has been playing an important role in industrial automation. Users want to improve programming efficiency by implementing code reuse and more intelligent code retrieval. Due to the heterogeneity of different PLC development environments, it is then necessary to design a computable knowledge model to semantically represent, organize, and utilize these diversified resources. Using the ontology technique is a common way to achieve the interoperability of heterogeneous systems. Aiming at this, we propose an ontology matching approach in this paper. Knowledge extraction and alignment are difficult for most of the knowledge graphs construction tasks, however, we are able to build the PLC domain knowledge graph with high accuracy and completeness by considering PLC domain characteristics, designing layered ontology, and implementing the matching process primarily on schema level instead of instance level.
Designing optimal models of nonlinear MIMO systems based on orthogonal polynomial neural networks

This paper presents a new method for modelling of dynamic systems by using specially designed orthogonal polynomial neural networks. These networks utilize the feature that the basis made of orthogonal functions can be used for approximation of arbitrary function, while their property of orthogonality enables optimal performances in the sense of both convergence time and approximation error. In this regard, generalized quasi-orthogonal polynomials, specifically tailored for the application in the modelling of complex dynamic systems with time-varying behaviour, are considered. Adaptivity of the designed model is achieved by using variable factors inside the orthogonal basis. The designed orthogonal neural network is applied in modelling of laboratory twin-rotor aero-dynamic system as a representative of nonlinear multiple input-multiple output systems. Detailed comparative analysis is performed for a different number of polynomials in expansion with the purpose of finding the optimal model in the sense of trade-off between model accuracy and complexity.
Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network

The rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the presence of interference signals such as spatiotemporal background mixed signals in the driving monitoring video sequence, the recognition accuracy of small targets such as human eyes is low. In this paper, an improved dual-stream convolutional network is proposed to recognize the safe driving behavior. Based on convolutional neural networks (CNNs), attention mechanism (AM) is integrated into a long short-term memory (LSTM) neural network structure, and the hybrid dual-stream AM-LSTM convolutional network channel is designed. The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. The time stream channel uses a single-shot multibox detector (SSD) algorithm to calculate the adjacent two frames of video sequence for the detection of small objects such as face and eyes. Then, AM-LSTM is used to fuse and classify dual-stream information. The self-built driving behavior video image set is built. ROC, accuracy rate, and loss function experiments are carried out in the FDDB database, VOT100 data set, and self-built video image set, respectively. Compared with CNN, SSD, IDT, and dual-stream recognition methods, the accuracy rate of this method can be improved by at least 1.4%, and the average absolute error in four video sequences can be improved by more than 2%. On the contrary, in the self-built image set, the recognition rate of doze reaches 68.3%, which is higher than other methods. The experimental results show that this method has good recognition accuracy and practical application value.
Decision tree pairwise metric learning against adversarial attacks

Distance Metric Learning has been used or paired with SoftMax Cross-Entropy loss to increase the discriminative power of deep learning classifiers against adversarial attacks. Most distance metric learning-based methods for adversarial detection adopt the standard Mahalanobis distance which only encodes the relative position information and therefore cannot capture the entire shape of complex data. In this research, we propose an alternative metric learning approach for adversarial sample classification. This approach integrates relative information as well as absolute pairwise information into a differentiable decision tree representation to guarantee a more robust classifier. We term this metric learning approach as differentiable decision tree pairwise metric learning (DTML). We demonstrate that DTML is more robust even under strong adversarial untargeted attacks compared with the single Mahalanobis distance-based defending methods on MNIST, CIFAR-10, and KDDCUP99 datasets.
Multivariate Statistical Analysis for Training Process Optimization in Neural Networks-Based Forecasting Models

Data forecasting is very important for electrical analysis development, transport dimensionality, marketing strategies, etc. Hence, low error levels are required. However, in some cases data have dissimilar behaviors that can vary depending on such exogenous variables as the type of day, weather conditions, and geographical area, among others. Commonly, computational intelligence techniques (e.g., artificial neural networks) are used due to their generalization capabilities. In spite of the above, they do not have a unique way to reach optimal performance. For this reason, it is necessary to analyze the data’s behavior and their statistical features in order to identify those significant factors in the training process to guarantee a better performance. In this paper is proposed an experimental method for identifying those significant factors in the forecasting model for time series data and measure their effects on the Akaike information criterion (AIC) and the Mean Absolute Percentage Error (MAPE). Additionally, we seek to establish optimal parameters for the proper selection of the artificial neural network model.
Quranic Script Optical Text Recognition Using Deep Learning in IoT Systems

Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things (IoT) systems, much research has been done in using machine learning methods to recognize IoT sensors data. This is particularly the case for optical character recognition of handwritten scripts. Recognizing text in images has several useful applications, including content-based image retrieval, searching and document archiving. The Arabic language is one of the mostly used tongues in the world. However, Arabic text recognition in imagery is still very much in the nascent stage, especially handwritten text. This is mainly due to the language complexities, different writing styles, variations in the shape of characters, diacritics, and connected nature of Arabic text. In this paper, two deep learning models were proposed. The first model was based on a sequence-to-sequence recognition, while the second model was based on a fully convolution network. To measure the performance of these models, a new dataset, called QTID (Quran Text Image Dataset) was devised. This is the first Arabic dataset that includes Arabic diacritics. It consists of 309,720 different 192 × 64 annotated Arabic word images, which comprise 2,494,428 characters in total taken from the Holy Quran. The annotated images in the dataset were randomly divided into 90%, 5%, and 5% sets for training, validation, and testing purposes, respectively. Both models were set up to recognize the Arabic Othmani font in the QTID. Experimental results show that the proposed methods achieve state-of-the-art outcomes. Furthermore, the proposed models surpass expectations in terms of character recognition rate, F1-score, average precision, and recall values. They are superior to the best Arabic text recognition engines like Tesseract and ABBYY FineReader.