Reviewerly/exHarmony huggingface dataset:
Text-to-speech system for low-resource language using cross-lingual transfer learning and data augmentation

Abstract Deep learning techniques are currently being applied in automated text-to-speech (TTS) systems, resulting in significant improvements in performance. However, these methods require large amounts of text-speech paired data for model training, and collecting this data is costly. Therefore, in this paper, we propose a single-speaker TTS system containing both a spectrogram prediction network and a neural vocoder for the target language, using only 30 min of target language text-speech paired data for training. We evaluate three approaches for training the spectrogram prediction models of our TTS system, which produce mel-spectrograms from the input phoneme sequence: (1) cross-lingual transfer learning, (2) data augmentation, and (3) a combination of the previous two methods. In the cross-lingual transfer learning method, we used two high-resource language datasets, English (24 h) and Japanese (10 h). We also used 30 min of target language data for training in all three approaches, and for generating the augmented data used for training in methods 2 and 3. We found that using both cross-lingual transfer learning and augmented data during training resulted in the most natural synthesized target speech output. We also compare single-speaker and multi-speaker training methods, using sequential and simultaneous training, respectively. The multi-speaker models were found to be more effective for constructing a single-speaker, low-resource TTS model. In addition, we trained two Parallel WaveGAN (PWG) neural vocoders, one using 13 h of our augmented data with 30 min of target language data and one using the entire 12 h of the original target language dataset. Our subjective AB preference test indicated that the neural vocoder trained with augmented data achieved almost the same perceived speech quality as the vocoder trained with the entire target language dataset. Overall, we found that our proposed TTS system consisting of a spectrogram prediction network and a PWG neural vocoder was able to achieve reasonable performance using only 30 min of target language training data. We also found that by using 3 h of target language data, for training the model and for generating augmented data, our proposed TTS model was able to achieve performance very similar to that of the baseline model, which was trained with 12 h of target language data.
Metaheuristic Algorithms Applied to Color Image Segmentation on HSV Space

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes' rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.
Green Computing: An Era of Energy Saving Computing of Cloud Resources

Cloud computing is a widely acceptable computing environment, and its services are also widely available.But the consumption of energy is one of the major issues of cloud computing as a green computing.Because many electronic resources like processing devices, storage devices in both client and server site and network computing devices like switches, routers are the main elements of energy consumption in cloud and during computation power are also required to cool the IT load in cloud computing.So due to the high consumption, cloud resources define the high energy cost during the service activities of cloud computing and contribute more carbon emissions to the atmosphere.These two issues inspired the cloud companies to develop such renewable cloud sustainability regulations to control the energy cost and the rate of CO 2 emission.The main purpose of this paper is to develop a green computing environment through saving the energy of cloud resources using the specific approach of identifying the requirement of computing resources during the computation of cloud services.Only required computing resources remain ON (working state), and the rest become OFF (sleep/hibernate state) to reduce the energy uses in the cloud data centers.This approach will be more efficient than other available approaches based on cloud service scheduling or migration and virtualization of services in the cloud network.It reduces the cloud data center's energy usages by applying a power management scheme (ON/OFF) on computing resources.The proposed approach helps to convert the cloud computing in green computing through identifying an appropriate number of cloud computing resources like processing nodes, servers, disks and switches/routers during any service computation on cloud to handle the energy-saving or environmental impact.
An Efficient Approach to Select Instances in Self-Training and Co-Training Semi-Supervised Methods

Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-supervised learning is attractive due to its potential to use labeled and unlabeled data to perform better than supervised learning. This paper consists of a study in the field of semi-supervised learning and implements changes on two well-known semi-supervised learning algorithms: self-training and co-training. In the literature, it is common to develop researches that change the structure of these algorithms, however, none of them proposes automating the labeling process of unlabeled instances, which is the main purpose of this work. In order to achieve this goal, three methods are proposed: FlexCon-G, FlexCon and FlexCon-C. The main difference among these methods is the way in which the confidence rate is calculated and the strategy used to select a label in each iteration. In order to evaluate the proposed methods&#x2019; performance, an empirical analysis is conducted, in which the performance of these methods has been evaluated on 30 datasets with different characteristics. The obtained results indicate that all three proposed methods perform better than the original self-training and co-training methods, in most analysed cases.
Performance-based emergency landing trajectory planning applying meta-heuristic and Dubins paths

Emergency Landing is a complex problem of optimal path planning of an impaired airplane in presence of obstacles, while the airplane performance characteristics have degraded. Some in-flight failures can affect the airplane dynamics and therefore the new dynamic constraints must be considered in flight planning to the desired landing site. This paper introduces a novel hybrid form of Dubins-simulated annealing (HDSA) optimization framework for emergency landing. The proposed architecture applies Dubins paths and Apollonius’ tangent line to generate candidate pieces of trajectories respecting the post-failure performance characteristics of the distressed airplane. The optimization pattern is used to select the optimal combination of the candidate trajectories based on the cost functions and the environmental constraints to lead the airplane to the desired landing site. Analytical performance-based equations are developed to achieve an admissible solution in emergency trajectory planning. The goal is to provide a general optimal framework, which can enhance the flight management system by assisting the pilot to plan the most suitable and admissible trajectory to the landing site in emergency flight conditions. The effectiveness of the proposed approach is demonstrated through simulations.
Hyperspectral Image Denoising via Low-Rank Representation and CNN Denoiser

Hyperspectral images (HSIs) are widely used in various tasks such as earth observation and target detection. However, during the imaging process, HSIs are often corrupted by various noises. In this article, we firstly investigate the advantages of traditional physical restoration models and the denoising convolutional neural networks (CNN) for HSIs denoising tasks. The sparse based low-rank representation can explore the global correlations in both the spatial and spectral domains, and the CNN-based denoiser can represent the deep prior which cannot be designed by traditional restoration models. Then, we propose a HSI denoising model with low-rank representation and CNN denoiser prior in the flexible and extensible plug-and-play framework by combining the advantages of the two methods. The proposed model is user-friendly, requiring no retraining. Simulated data experiments show that, compared with competitive methods, the proposed one achieves better denoising results for both additive Gaussian noise and Poissonian noise in various quantitative evaluation indicators. Real data experiments show that the proposed model yields the best performance.
An improved Deeplabv3+ semantic segmentation algorithm with multiple loss constraints

Aiming at the problems of low segmentation accuracy and inaccurate object boundary segmentation in current semantic segmentation algorithms, a semantic segmentation algorithm using multiple loss function constraints and multi-level cascading residual structure is proposed. The multi-layer cascaded residual unit was used to increase the range of the network layer receptive field. A parallel network was constructed to extract different depth feature information, then different depth feature information and encoder output features are fused to obtain multiple outputs feature which build multiple losses with the label, thereby constraining the model to optimize the network. The proposed network was evaluated on Cityscapes and CamVid datasets. The experimental results show that the mean Intersection over Union ratio (MIoU) of the proposed algorithm is 3.07% and 3.59% higher than the original Deeplabv3+ algorithm, respectively.
Attention enhanced capsule network for text classification by encoding syntactic dependency trees with graph convolutional neural network

Text classification is a fundamental task in many applications such as topic labeling, sentiment analysis, and spam detection. The text syntactic relationship and word sequence are important and useful for text classification. How to model and incorporate them to improve performance is one key challenge. Inspired by human behavior in understanding text. In this paper, we combine the syntactic relationship, sequence structure, and semantics for text representation, and propose an attention-enhanced capsule network-based text classification model. Specifically, we use graph convolutional neural networks to encode syntactic dependency trees, build multi-head attention to encode dependencies relationship in text sequence, merge with semantic information by capsule network at last. Extensive experiments on five datasets demonstrate that our approach can effectively improve the performance of text classification compared with state-of-the-art methods. The result also shows capsule network, graph convolutional neural network, and multi-headed attention has integration effects on text classification tasks.
A Novel Deep Learning Approach for Short Term Photovoltaic Power Forecasting Based on GRU-CNN Model

The integration of photovoltaic power brings the key to clean energy. However, the increasing proportion of photovoltaic (PV) energy in power systems due to the random and intermittent nature of solar energy resources is causing difficulties for system operators to dispatch PV power stations. To reduce the negative influence of the use of PV power, it is great significant to predict PV power accurately. In this paper, we propose a high-precision hybrid neural network model that employs Gated Recurrent Units (GRU) and Convolution Neural Network (CNN) to build a GRU-CNN model to forecast PV system output power. The proposed framework has two major phases. Firstly, the sample data is divided into training set and test set. For this, the temporal characteristics of the data set are extracted using a GRU model and the spatial characteristics are obtained using the CNN model. Secondly, the final predicted PV power is obtained through the output layer. The forecasting accuracy of GRU-CNN is determined by the mean absolute error (MAE), mean square error (MSE), determination coefficient (R 2 ) and root mean square error (RMSE) values. The findings of the comparison experiments show that the GRU-CNN model has better accuracy than some deep learning methods, including, GRU, CNN and long-short term memory model (LSTM).
Passive Sonar Target Tracking Based on Deep Learning

At present, the tracking accuracy of underwater passive target tracking is often limited due to models that are overly simple, with low complexity, poor universality, and an inability to learn. In this paper, a cubature Kalman filter (CKF) algorithm based on a gated recurrent unit (GRU) network is proposed. The filter innovation, prediction error, and filter gain obtained from the CKF are used as the input to the GRU network, and the filter error value is used as the output to train the network. End-to-end online learning is carried out using the designed fully connected network, and the current state of the target is predicted. In this paper, a deep neural network based on the GRU architecture is used to convert the tracking prediction problem into a time series prediction problem in the field of artificial intelligence, and its strong fitting ability is used to resolve the uncertainty of the target motion. Simulation results show that an unmanned underwater vehicle (UUV) state estimation method based on the GRU filter proposed in this paper offers better accuracy and stability than the traditional state estimation method.
How to choose the size of facial areas of interest in interactive eye tracking

Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer’s test conditions, validation is essential with regard to the quality of gaze data and other factors potentially threatening the validity of this signal. In this study, we evaluated the impact of accuracy and areas of interest (AOIs) size on the classification of simulated gaze (fixation) data. We defined AOIs of different sizes using the Limited-Radius Voronoi-Tessellation (LRVT) method, and simulated gaze data for facial target points with varying accuracy. As hypothesized, we found that accuracy and AOI size had strong effects on gaze classification. In addition, these effects were not independent and differed in falsely classified gaze inside AOIs (Type I errors; false alarms) and falsely classified gaze outside the predefined AOIs (Type II errors; misses). Our results indicate that smaller AOIs generally minimize false classifications as long as accuracy is good enough. For studies with lower accuracy, Type II errors can still be compensated to some extent by using larger AOIs, but at the cost of more probable Type I errors. Proper estimation of accuracy is therefore essential for making informed decisions regarding the size of AOIs in eye tracking research.
Neural Network Design using a Virtual Reality Platform

The evolution of Deep Learning (DL), a subset of machine learning, has made their use very effective in many artificial intelligence(AI) fields. In parallel Virtual Reality is going wide in many applications thanks to the proliferation of cameras in mobile devices and improved processing efficiency. Data visualization in deep learning is a fundamental element for which it can benefit from the advantages offered by the visualization of the VR for the development of the models. In addition, the researchers can widely use the editing of images and videos in the machine learning process to design a convolutional network suitable for image recognition. In this study, we want to demonstrate the usefulness of this approach in collecting data within virtual reality to train and optimize a convolutional neural network used to recognize human activities (HAR).
An Image Encryption Scheme Based on Logistic Quantum Chaos

This paper proposes an image encryption scheme based on logistic quantum chaos. Firstly, we use compressive sensing algorithms to compress plaintext images and quantum logistic and Hadamard matrix to generate the measurement matrix. Secondly, the improved flexible representation of the quantum images (FRQI) encoding method is utilized for encoding the compressed image. The pixel value scrambling operation of the encoded image is realized by rotating the qubit around the axis. Finally, the quantum pixel is encoded into the pixel value in the classical computer, and the bit-level diffusion and scrambling are performed on it. Numerical analysis and simulation results show that our proposed scheme has the large keyspace and strong key sensitivity. The proposed scheme can also resist standard attack methods such as differential attacks and statistical analysis.
Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition

In recent years, with the growth of digital media and modern imaging equipment, the use of video processing algorithms and semantic film and image management has expanded. The usage of different video datasets in training artificial intelligence algorithms is also rapidly expanding in various fields. Due to the high volume of information in a video, its processing is still expensive for most hardware systems, mainly in terms of its required runtime and memory. Hence, the optimal selection of keyframes to minimize redundant information in video processing systems has become noteworthy in facilitating this problem. Eliminating some frames can simultaneously reduce the required computational load, hardware cost, memory and processing time of intelligent video-based systems. Based on the aforementioned reasons, this research proposes a method for selecting keyframes and adaptive cropping input video for human action recognition (HAR) systems. The proposed method combines edge detection, simple difference, adaptive thresholding and 1D and 2D average filter algorithms in a hierarchical method. Some HAR methods are trained with videos processed by the proposed method to assess its efficiency. The results demonstrate that the application of the proposed method increases the accuracy of the HAR system by up to 3% compared to random image selection and cropping methods. Additionally, for most cases, the proposed method reduces the training time of the used machine learning algorithm.
Aligning Subtitles in Sign Language Videos

The goal of this work is to temporally align asynchronous subtitles in sign language videos. In particular, we focus on sign-language interpreted TV broadcast data comprising (i) a video of continuous signing, and (ii) subtitles corresponding to the audio content. Previous work exploiting such weakly-aligned data only considered finding keyword-sign correspondences, whereas we aim to localise a complete subtitle text in continuous signing. We propose a Transformer architecture tailored for this task, which we train on manually annotated alignments covering over 15K subtitles that span 17.7 hours of video. We use BERT subtitle embeddings and CNN video representations learned for sign recognition to encode the two signals, which interact through a series of attention layers. Our model outputs frame-level predictions, i.e., for each video frame, whether it belongs to the queried subtitle or not. Through extensive evaluations, we show substantial improvements over existing alignment baselines that do not make use of subtitle text embeddings for learning. Our automatic alignment model opens up possibilities for advancing machine translation of sign languages via providing continuously synchronized video-text data.
Building a novel physical design of a distributed big data warehouse over a Hadoop cluster to enhance OLAP cube query performance

Improving OLAP (Online Analytical Processing) query performance in a distributed system on top of Hadoop is a challenging task. An OLAP Cube query comprises several relational operations, such as selection, join, and group-by aggregation. It is well-known that star join and group-by aggregation are the most costly operations in a Hadoop database system. These operations indeed increase network traffic and may overflow memory; to overcome these difficulties, numerous partitioning and data load balancing techniques have been proposed in the literature. However, some issues remain questionable, such as decreasing the Spark stages and the network I/O for an OLAP query being executed on a distributed system. In a precedent work, we proposed a novel data placement strategy for a big data warehouse over a Hadoop cluster. This data warehouse schema enhances the projection, selection, and star-join operations of an OLAP query, such that the system’s query-optimizer can perform a star join process locally, in only one spark stage without a shuffle phase. Also, the system can skip loading unnecessary data blocks when executing the predicates. In this paper, we extend our previous work with further technical details and experiments, and we propose a new dynamic approach to improve the group-by aggregation. To evaluate our approach, we conduct some experiments on a cluster with 15 nodes. Experimental results show that our method outperforms existing approaches in terms of OLAP query evaluation time.
Simple, Efficient, Highly Secure, and Multiple Purposed Method on Data Cryptography

Some digital data circulated through various social media, regardless of its nature, requires high-level protection and security for various reasons.In this research, a multi-purpose method will be presented to be used in encrypting SMS messages, text files regardless of their size, digital images of all kinds and sizes, and audio files of all kinds and sizes.The proposed method will be examined to prove its efficiency, and the practical results will be compared with the implementation results of other internationally approved methods to show the extent to which the method improves efficiency indicators.It will be shown how to use digital color image to generate a highly secure private key which will make the process of hacking impossible.The Quality of the encrypted and decrypted images will be examined to justify the use of the proposed method.
Technology on the move, Consumer Electronics Show 2022: the evolving metaverse and much more

Purpose The purpose of this study is to showcase exciting new technology products coming to the marketplace. Design/methodology/approach The conference report was written by LHTN Editor on CES2022, forecasting new technology trends and products of interest to librarians and others. Findings Several new products and trends are featured in this conference report. Originality/value Original review from attending the CES2020 conference in Janurary 2022.