Reviewerly/exHarmony huggingface dataset:
Controlling sudden transition from classical to quantum decoherence via non-equilibrium environments

Abstract We investigate the freezing and sudden transition in the dynamical behavior of quantum and classical correlations in a system composed of two identical non-interacting qubits locally subjected to their own non-equilibrium environments. In contrast to the equilibrium case, one can observe striking results when a bipartite quantum system couples with the non-equilibrium dephasing environment with non-stationary and non-Markovian features. Remarkably, the finite time interval in which the quantum correlation remains impervious to decoherence can be further prolonged as the environment deviates from equilibrium. This reveals that the non-equilibrium parameter provides an alternative tool to efficiently control the appearance of a sudden transition in the decay rates of correlations and their immunity towards the decoherence. Furthermore, for certain initial states, the appearance of another time-interval over which quantum correlation remains constant and the revival of classical correlation not only depends on the non-Markovianity but also on the non-equilibrium parameter.
Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric

Adaptive Resonance Theory (ART)-based growing self-organizing clustering is one of the most promising approaches for unsupervised topological clustering. In our previous study, we proposed a Topological Correntropy induced metric based ART (TCA) and shown its superior performance. However, TCA suffers from a data-dependent parameter and a complicated network creation process which lead to inefficient learning. This paper aims to solve problems of TCA by implementing an automatic parameter specification mechanism and simplifying a learning algorithm. Experimental results show that the proposed algorithm in this paper successfully solved the above problems.
ShadowPlay2.5D

An immersive experience brought about by virtual reality can potentially enhance the appreciation of classical Chinese poetry, which is difficult to describe clearly in everyday language or ordinary media. However, making 3-dimensional illustrations for a 360-degree display in virtual reality is usually a labor-intensive and time-consuming procedure and hard to master for non-professional media creators, such as teachers. Motivated by the homology theory of classical Chinese poetry and painting, we propose an image-based approach of building 2.5-dimensional immersive stories to visualize classical Chinese poetry. Specifically, using Chinese shadow play as a metaphor, we have designed and implemented ShadowPlay2.5D , a sketch-based authoring tool to help novices create 360-degree videos of classical Chinese poetry easily. To ensure coverage of the diverse themes in Chinese poetry and preserve the sense of culture, we build a Chinese ink-painting style image repository of essential poetic elements identified via crowdsourcing. To facilitate construction of 2.5-dimensional scenes, we design features that support puppet-like animation, instancing, and camera organization in a 3-dimensional environment. Through two user studies, we show that ShadowPlay2.5D can help novices make a short 360-degree video in about 10--15 minutes, and the 2.5D stylized illustrations created can bring about a better immersive experience for poetry appreciation.
A Preference-Based Evolutionary Biobjective Approach for Learning Large-Scale Fuzzy Cognitive Maps: An Application to Gene Regulatory Network Reconstruction

Learning large-scale fuzzy cognitive maps (FCMs) with the sparse attribute automatically from time series without prior knowledge remains a challenging problem. Most existing automated learning methods were applied to learn small-scale FCMs, and the learned FCMs are much denser than the maps constructed by human experts. Learning FCMs is the procedure of judging whether there are connecting edges and determining the weights of connecting edges. Thus, we transform the problem of learning FCMs into a biobjective optimization problem with two objects of minimizing the measure error and the number of nonzero entries, respectively. To solve this optimization problem, a preference-based iterative thresholding evolutionary biobjective optimization algorithm for learning FCMs is proposed. The strategy focuses on the knee area of the Pareto front (PF) with preference on the solutions near the true sparsity. Moreover, an initialization operator based on random forest is proposed to increase the speed of convergence toward the PF. The experiments on large-scale synthetic data with varying sizes and densities and the application to the gene regulatory network reconstruction problem have been conducted to demonstrate that our proposal matches or exceeds the existing state-of-the-art FCM learning approaches in most cases in terms of four measures, namely, Data_Error, Out_of_Sample_Error, Model Error, and SS_Mean. The Data_Error obtained by the proposed method can achieve 3.07E-06 even when the number of nodes reaches 200. We also demonstrate the effectiveness of the proposed initialization operator and the preference-based strategy, which can result in a fast convergence speed and higher accuracy.
On nested infinite occupancy scheme in random environment

Abstract We consider an infinite balls-in-boxes occupancy scheme with boxes organised in nested hierarchy, and random probabilities of boxes defined in terms of iterated fragmentation of a unit mass. We obtain a multivariate functional limit theorem for the cumulative occupancy counts as the number of balls approaches infinity. In the case of fragmentation driven by a homogeneous residual allocation model our result generalises the functional central limit theorem for the block counts in Ewens’ and more general regenerative partitions.
Utility-Driven Mining of High Utility Episodes

Sequence data, e.g., complex event sequence, is more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this paper, we propose an efficient utility mining approach namely UMEpi: Utility Mining of high-utility Episodes from complex event sequence. We propose the concept of remaining utility of episode, and achieve a tighter upper bound, namely episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategy can achieve better improvements in mining efficiency. Finally, experiments on two real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequence, while state-of-the-art algorithms fail to return the correct results. Besides, the improved variants of UMEpi outperforms the baseline.
Regularising neural networks for future trajectory prediction via inverse reinforcement learning framework

Predicting distant future trajectories of agents in a dynamic scene is challenging because the future trajectory of an agent is affected not only by their past trajectory but also the scene contexts. To tackle this problem, the authors propose a model based on recurrent neural networks, and a novel method for training this model. The proposed model is based on an encoder–decoder architecture where the encoder encodes inputs (past trajectory and scene context information), while the decoder produces a future trajectory from the context vector given by the encoder. To make the proposed model better utilise the scene context information, the authors let the encoder predict the positions in the past trajectory and a reward function evaluate the positions along with the scene context information generated by the positions. The reward function, which is simultaneously trained with the proposed model, plays the role of a regulariser for the model during the simultaneous training. The authors evaluate the proposed model on several public benchmark datasets. The experimental results show that the prediction performance of the proposed model is greatly improved by the proposed regularisation method, which outperforms the‐state‐of‐the‐art models in terms of accuracy.
EWPTNN: An Efficient Workload Prediction Model in Cloud Computing Using Two-Stage Neural Networks

Allocating insufficient resources to the cloud applications, leading to the loss of revenues, consumers and, Quality-of-Services (QoS). Another side, allocating resources more than is needed leading to the wastage of energy and cost to maintain the resources like servers, datacenters and, network bandwidth etc. So, this problem can be solved with predictive scaling methods by using machine learning approaches, which can estimate the future needed resources and, performing scaling operations in advance. To perform accurate scaling operations, authors presented an enhanced prediction model based on a neural networks, which is a combination of a classification and prediction model to predict the accurate utilization of resources. At the first stage, the proposed model categorizes the resources into three classes like over, normal and, under adaptively based on the utilization of resources. In the next stage, it predicts the future utilization of resources by using neural network regression model based on the classification results. The experimental results showed that the proposed model achieved accurate results.
Federated Query processing for Big Data in Data Science

As the number of databases continues to grow data scientists need to use data from different sources to run machine learning algorithms for analysis. Data science results depend upon the quality of data been extracted. The objective of this research paper is to implement a federated query processing framework which extracts data from different data sources and stores the result datasets in a common in-memory data format. This helps data scientists to perform their analysis and execute machine learning algorithms using different data engines without having to convert the data into their native data format and improve the performance.
Enhancement of Cross Validation Using Hybrid Visual and Analytical Means with Shannon Function

The algorithm of k-fold cross validation is actively used to evaluate and compare machine learning algorithms. However, it has several important deficiencies documented in the literature along with its advantages. The advantages of quick computations are also a source of its major deficiency. It tests only a small fraction of all the possible splits of data, on training and testing data leaving untested many difficult for prediction splits. The associated difficulties include bias in estimated average error rate and its variance, the large variance of the estimated average error, and possible irrelevance of the estimated average error to the problem of the user. The goal of this paper is improving the cross validation approach using the combined visual and analytical means in a hybrid setting. The visual means include both the point-to-point mapping and a new point–to-graph mapping of the n-D data to 2-D data known as General Line Coordinates. The analytical means involve the adaptation of the Shannon function to obtain the worst case error estimate. The method is illustrated by classification tasks with simulated and real data.
Secure image transmission through crypto‐OFDM system using Rubik's cube algorithm over an AWGN channel

Summary Confidentiality of information must be maintained when it is required to be transmitted over a communication channel or when it is stored in a computer for further information access. Establishing a highly reliable and secure means of wireless communication for the transfer of digital data (text, images, audio, and video) from source to destination is becoming a prime requirement in present‐day wireless communications. Security can be achieved at the network level as well as at the data level. Data‐level security is made through cryptographic techniques. This paper emphasizes data‐level security aspects in a wireless communication system for the secure transmission of images over an AWGN channel condition. Here, a crypto orthogonal frequency division multiplexing system (crypto‐OFDM system) is designed using Rubik's cube encryption algorithm scheme for secured transmission of images under the MATLAB environment. The quality of the image transmission is analyzed using peak signal to noise ratio (PSNR) and BER at different SNR conditions. The quality of the encryption algorithm was also tested with statistical metrics, which are the histogram, NPCR, UACI, entropy, correlation coefficient analysis, etc. The numerical results reveal that a DCT‐based crypto‐OFDM system with Rubik's cube algorithm show a better performance over earlier cryptosystems and also perform superior to the original/basic OFDM system. The statistical analysis tests prove that Rubik's cube algorithm is one of the most robust algorithms for security point of view and is easy to implement, as Rubik's cube algorithm makes use of natural characteristics of pixel values, which form the basis for designing the secret key .
Side-Channel-Attack Resistant Dual-Rail Asynchronous-Logic AES Accelerator Based on Standard Library Cells

We present a side-channel-attack (SCA) resistant Advanced Encryption Standard (AES) accelerator by means of asynchronous-logic (async) based on the standard library cells. To mitigate SCA, we adopt the dual-rail logic, and propose a delayed completion tree (to introduce delay variations) and the data flow control (to halt reset operation at the last round). We further perform a comprehensive SCA evaluation (with 7 attacking/power models) by means of power simulations. To the best of our knowledge, such comprehensive SCA evaluation has never been reported for other async AES or its sub-block designs. Based on the basis of 5k power simulations, we show that our proposed async AES accelerator are unbreakable. Our proposed async AES accelerator occupies 420μm×420μm @ 65nm CMOS and dissipates 2nJ/encryption @ 1.2V.
3D Action Recognition Exploiting Hierarchical Deep Feature Fusion Model

Numerous existing handcrafted feature-based and conventional machine learning-based approaches cannot seize the intensive correlations of skeleton structure in the spatiotemporal dimension. On another hand, some modern methods exploiting Long Short Term Memory (LSTM) to learn temporal action attribute, which lack an efficient scheme of revealing high-level informative features. To handle the aforementioned issues, this research introduces a novel hierarchical deep feature fusion model for 3D skeleton-based human action recognition, in which the deep information for modeling human appearance and action dynamic is gained by Convolutional Neural Networks (CNNs). The deep features of geometrical joint distance and orientation are extracted via a multi-stream CNN architecture to uncovering the hidden correlations in both the spatial and temporal dimensions. The experimental results on the NTU RGB+D dataset demonstrates the superiority of the proposed fusion model against several recently deep learning (DL)-based action recognition approaches.
Design and Implementation of AR-Supported System for Piano Learning

In this paper, we propose a system to support piano class learning with Augmented Reality (AR) technology. The system composes of several key functionalities: 1) real-time highlighted key pressing, 2) virtual hand models, and 3) instruction mapping between music sheet and keyboard. We aim to boost learners' interest in learning piano by enhancing the study experience using AR technology.
Automatic Grading System for Diagnosis of Breast Cancer Exploiting Co-occurrence Shearlet Transform and Histogram Features

Breast cancer (BC) is one of the most commonly reported health issues worldwide, especially in females. Early detection and diagnosis of BC can greatly reduce mortality rates. Samples obtained with different imaging methods such as mammography, computerized tomography, magnetic resonance, ultrasound, and biopsy are used in the diagnosis of BC. Histopathological images obtained from a biopsy contain vital information about the stage of the BC. Computer-aided systems are important tools to assist pathologists in the early detection of BC. In the current study, the use of gray-level co-occurrence matrix (GLCM) of Shearlet Transform (ST) coefficients were first scrutinized as textural features. ST is an advanced decomposition-based method that can analyze images in various directions and is sensitive to edge singularities. These features make ST more robust than other decomposition methods such as Fourier and wavelet. Color channel histogram features were also utilized for a second level of evaluation in the diagnosis of the BC stage. These features are considered one of the most important building blocks that pathologists consider in the course of grading histopathological images. Then, by combining these two features, the classification results were re-assessed utilizing Support Vector Machine (SVM) as a classifier. The assessments were performed on a BreaKHis dataset containing benign and malignant histopathological samples. The average accuracy scores were reported as being 98.2%, 97.2%, 97.8%, and 97.3% in the sub-databases with 40×, 100×, 200×, and 400× magnification factors, respectively. The obtained results showed that the proposed method was quite efficient in histopathological image classification. Despite the relative simplicity of the approach, the obtained results were far superior to previously reported results.
User Behavior Classification in Encrypted Cloud Camera Traffic

Home surveillance cameras have been widely used in recent years, however, what comes next is the increasing events of users' privacy disclosure. Most cameras use video compression technologies such as H.26X and MPEG to reduce the stream size during transmission on the purpose to transfer higher definition(HD) video with limited bandwidth. Although traffic is encrypted, these video differential encoding techniques can cause traffic patterns to change as users' behavior changes. Therefore, it is very meaningful to mine user privacy behavior hidden behind the surveillance traffic based on traffic statistical features. In this paper, we collected a large amount of surveillance video traffic, including 9 kinds of daily life behaviors, such as watching TV, switching door, sweeping the floor, etc. According to frequency distribution sequences of packet length, probability transition matrix of packet length and other statistical features, we used a variety of models to classify users' daily life behaviors. The results show that the random forest classifier and the AlexNet classifier can achieve a macro-averaging F1-score of 97.23% and 97.89% respectively. The performance of our approach reveals that the users' daily routine can be accurately constructed only through video traffic, which is a potentially huge security issue for the users. Moreover, this work has important reference value for camera manufacturers to improve user privacy protection.
Phoneme Based Model for Gender Identification and Adult-Child Classification

Speech is the primary way of communicating messages from a speaker to a listener. The listener can identify details about the speaker's personality, age, gender, accent, emotions from the voiced signal. In this paper, we address the gender identification and adult-child classification using the phoneme-based parametric model. The multi-component amplitude and frequency modulated (AFM) signal model is suitable for the entire duration of a phoneme, and the estimation of model parameters is robust to various recording conditions. The gender identification and adult-child classification are done using the features extracted from the phoneme-based AFM signal model and applying the support vector machines (SVM) with the k-fold cross-validation (CV). The experimental results have been performed in the various recording conditions and show that the proposed technique is suitable for gender identification and age classification independent of spoken text, emotion of the speaker, environment and microphone set-up. It is demonstrated that the proposed model-based technique performs with a high probability of correct identification/classification.
Airborne imaging spectroscopy of igneous layered complex and their mapping using different spectral enhancement conjugated support vector machine models

(2020). Airborne imaging spectroscopy of igneous layered complex and their mapping using different spectral enhancement conjugated support vector machine models. Geocarto International. Ahead of Print.
Bangla Sign Language Recognition with Skin Segmentation and Binary Masking

The barrier between the hearing and speech impaired and society has been bridged through sign language for ages. Hereby, we present a system that recognizes Bangla Sign Language (BdSL) through the segmentation of hand gestures, binary masking and SVM classification. In the system, the features of different notations have been carefully distinguished and extracted to improve accuracy compared to the existing work. Through conversion into YCbCr color space and Fuzzy C-means clustering, a binary image is produced in the system which is later undergone binary masks designed for particular skin segmentations. Since YCbCr color space provides an effective use of chrominance information for modeling the human skin color, this color space has been used to pre-process the images. The dataset used in this system has been created and curated to perform under the manual feature extraction process. Extracting about 18 different features of the hand gestures of 10 alphabets, they are compared, classified and trained using a multiclass SVM classifier. Multiclass SVM classifier is extensively used for classification of high dimensional spaces and also effective for a smaller dataset. Using this methodology, the system can achieve an accuracy of 99.8% for static hand poses.
Signal Detection in Uplink Pilot-Assisted Multi-User MIMO Systems with Deep Learning

In this paper, we present a deep learning model for signal detection in uplink pilot-assisted multi-user MIMO systems and design two system frameworks for two cases of channel information at receivers, respectively. First, we consider the case that the channel matrix is known at base station (BS), the deep neural network (DNN) model can be seen as a detector and the transmission symbols are detected through channel matrix and received signal. Next, we consider the case in which the channel matrix is unknown at BS. The pilot-assisted DNN model we design can be regarded as a combination of channel estimator and signal detector. In this pilot-assisted DNN model, the channel information is indirectly reflected by pilot signal and the transmission symbols can be detected directly. The training data used for DNN model training is generated by simulation. When training stage is completed, the trained DNN model can be deployed for signal detection. The simulation results shown that the DNN-based detector with perfect channel state information can get better performance than conventional minimum mean squared error (MMSE) detector. Moreover, when channel matrix is unknown at BS, the pilot-assisted DNN-based detector can recover transmitted signal directly in an end-to-end manner without explicitly estimating channel and can get better performance than MMSE detector with least square (LS) channel estimation.