Reviewerly/exHarmony huggingface dataset:
Encrypted Speech Recognition Using Deep Polynomial Networks

The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises concerns about the privacy and security issues. The recognition results generated in cloud may also reveal some sensitive information. This paper proposes a deep polynomial network (DPN) that can be applied to the encrypted speech as an acoustic model. It allows clients to send their data in an encrypted form to the cloud to ensure that their data remains confidential, at mean while the DPN can still make frame-level predictions over the encrypted speech and return them in encrypted form. One good property of the DPN is that it can be trained on unencrypted speech features in the traditional way. To keep the cloud away from the raw audio and recognition results, a cloud-local joint decoding framework is also proposed. We demonstrate the effectiveness of model and framework on the Switchboard and Cortana voice assistant tasks with small performance degradation and latency increased comparing with the traditional cloud-based DNNs.
Speaker Characterization Using TDNN-LSTM Based Speaker Embedding

In this paper we propose speaker characterization using time delay neural networks and long short-term memory neural networks (TDNN-LSTM) speaker embedding. Three types of front-end feature extraction are investigated to find good features for speaker embedding. Three kinds of data augmentation are used to increase the amount and diversity of the training data. The proposed methods are evaluated with the National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) tasks. Experimental results show that the proposed methods achieve a decision cost of 0.400 with the pooled SRE 2018 development set with a single system. In addition, by applying simple average score combination on the outputs of 12 systems, the proposed methods achieve an equal error rate (EER) of 5.56% and a minimum decision cost function of 0.423 with the SRE 2016 evaluation set.
Modified single-output Chebyshev-polynomial feedforward neural network aided with subset method for classification of breast cancer

Breast cancer has become one of the leading causes of death in female population due to its high morbidity and mortality. However, the treatment options for benign or malignant tumors are different, which makes the diagnosis of breast cancer important. In this paper, the subset method is proposed to improve the single-output Chebyshev-polynomial neural network (SOCPNN), and the modified SOCPNN has a 100.00% testing accuracy in pattern classification on the Wisconsin breast cancer dataset. Specifically, the subset method generates the optimal number of cross-validation folds and constructs the initial structure of the modified SOCPNN automatically and rapidly, with a group of basis functions based on Chebyshev-polynomials exploited to activate the neural network. In addition, the weights of the hidden-layer neurons are directly determined by the weights-direct-determination (WDD) method. Besides, the optimal structure of the neural network is determined by a growing method. Moreover, to improve the generalization performance, the multi-fold cross-validation algorithm is exploited in the modified SOCPNN. Finally, comparative experiments on the Wisconsin breast cancer dataset are conducted and the results show that the modified SOCPNN has higher accuracy in classification of breast cancer on this dataset compared to the traditional machine learning algorithms.
Learning to Rank: A Progressive Neural Network Learning Approach

Learning to rank is an essential component in an information retrieval system. The state-of-the-art ranking systems are often based on an ensemble of classifiers, such as Random Forest or LambdaMART, which aggregates the ranking outputs produced by thousands of classifiers. The storage and computation requirement of an ensemble model is usually very high, imposing a significant operating cost to the retrieval system. To tackle this problem, we propose an algorithm that adaptively learns a single heterogeneous feedforward network architecture, composing of Generalized Operational Perceptrons, given a ranking problem. Experimental results in web search ranking and image retrieval tasks show that the proposed algorithm compares favourably to the related algorithms.
Process maturity models: Classification by application sectors and validities studies

Abstract A variety of maturity models have been developed and evolved as a tool for assessments/benchmarking and continuous improvements for the past two decades. The rapidly increasing number of maturity models led to problems in locating proper maturity models and identifying model validities. To identify maturity models and their validities as well as a classification of models across industrial sectors, a mapping study was applied to initially retrieved 2381 articles on maturity models published in peer‐reviewed journals between 1993 and June 2015. We identified 194 maturity models from 285 articles using a set of quality assessment steps. Among those maturity models, besides the five partially validated ones, this study also identified 26 maturity models that were empirically tested on the basis of tested hypotheses and associated validity types, such as predictive validity and unidimensionality. Additionally, despite the wide presence of maturity models in most application sectors, validated models were observed only in five sectors, eg, IT, industrials, and general. Practitioners and researchers as well as standard organizations can utilize our results to find out maturity models meeting their needs, to identify their validities, and/or to seek research opportunities.
A Sense Annotated Corpus for All-Words Urdu Word Sense Disambiguation

Word Sense Disambiguation (WSD) aims to automatically predict the correct sense of a word used in a given context. All human languages exhibit word sense ambiguity, and resolving this ambiguity can be difficult. Standard benchmark resources are required to develop, compare, and evaluate WSD techniques. These are available for many languages, but not for Urdu, despite this being a language with more than 300 million speakers and large volumes of text available digitally. To fill this gap, this study proposes a novel benchmark corpus for the Urdu All-Words WSD task. The corpus contains 5,042 words of Urdu running text in which all ambiguous words (856 instances) are manually tagged with senses from the Urdu Lughat dictionary. A range of baseline WSD models based on n -gram are applied to the corpus, and the best performance (accuracy of 57.71%) is achieved using word 4-gram. The corpus is freely available to the research community to encourage further WSD research in Urdu.
Distributed Bayesian Estimation with Low-rank Data: Application to Solar Array Processing

In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV installations within a single ZIP code. Additionally, we also derive a Bayesian lower bound on the shading parameter's mean squared estimation error. The results are promising and show that we can successfully estimate the fraction of partial shading in solar installations that can usually go unnoticed.
Cloze testing for comprehension assessment: The HyTeC-cloze

Although there are many methods available for assessing text comprehension, the cloze test is not widely acknowledged as one of them. Critiques on cloze testing center on its supposedly limited ability to measure comprehension beyond the sentence. However, these critiques do not hold for all types of cloze tests; the particular configuration of a cloze determines its validity. We review various cloze configurations and discuss their strengths and weaknesses. We propose a new cloze procedure specifically designed to gauge text comprehension: the Hybrid Text Comprehension cloze (HyTeC-cloze). It employs a hybrid mechanical-rational deletion strategy and semantic scoring of answers. The procedure was tested in a large-scale study, involving 2926 Dutch secondary school students with 120 unique cloze tests. Our results show that, in terms of reliability and validity, the HyTeC-cloze matches and sometimes outperforms standardized tests of reading ability.
Recent advances in mobile touch screen security authentication methods: A systematic literature review

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user's stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquiry.
Multiscale transform-based secured joint efficient medical image compression-encryption using symmetric key cryptography and ebcot encoding technique

Due to the huge advancement in technology, digitizing the multimedia content like text, images and videos has become easier. Everyday huge amounts of multimedia content are shared through the social networks using internet. Sometimes this multimedia content can be hacked by the hackers. This will lead to the misuse of the data. On the other hand, the medical content needs high security and privacy. Motivated by this, joint secured medical image compression–encryption mechanisms are proposed in this paper using multiscale transforms and symmetric key encryption techniques. The multiscale transforms involved in this paper are wavelet transform, bandelet transform and curvelet transform. The encryption techniques involved in this paper are international data encryption algorithm (IDEA), Rivest Cipher (RC5) and Blowfish. The encoding technique used in this paper is embedded block coding with truncation (EBCOT). Experimental results are done for the proposed works and evaluated by using various parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Image Quality Index (IQI) and Structural Similarity Index (SSIM), Average Difference (AD), Normalized Cross-correlation (NK), Structural Content (SC), Maximum difference (MD), Laplacian Mean Squared Error (LMSE) and Normalized Absolute Error (NAE). It is justified that the proposed approaches in this paper yield good results.
A Factorial Deep Markov Model for Unsupervised Disentangled Representation Learning from Speech

We present the Factorial Deep Markov Model (FDMM) for representation learning of speech. The FDMM learns disentangled, interpretable and lower dimensional latent representations from speech without supervision. We use a static and dynamic latent variable to exploit the fact that information in a speech signal evolves at different time scales. Latent representations learned by the FDMM outperform a baseline i-vector system on speaker verification and dialect identification while also reducing the error rate of a phone recognition system in a domain mismatch scenario.
Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning.
Towards Better Confidence Estimation for Neural Models

In this work we focus on confidence modeling for neural network based text classification and sequence to sequence models in the context of Natural Language Understanding (NLU) tasks. For most applications, the confidence of a neural network model in it's output is computed as a function of the posterior probability, determined via a softmax layer. In this work, we show that such scores can be poorly calibrated [1]. We propose new ensemble and gradient based features that predict model uncertainty and confidence. We evaluate the impact of these features through a gradient boosted decision tree (GBDT) framework to produce calibrated confidence scores. We demonstrate that the performance of our proposed approach surpasses the baseline across multiple tasks. Moreover, we show that this method produces confidence scores which are better suited for Out-Of-Distribution(OOD) classification when compared to the baseline.
Explicit Haar–Schauder multiwavelet filters and algorithms. Part II: Relative entropy-based estimation for optimal modeling of biomedical signals

Biomedical signal/image processing and analysis are always fascinating tasks in scientific researches, both theoretical and practical. One of the powerful tools in such topics is wavelet theory which has been proved to be challenging since its discovery. One of the best measures of the optimality of reconstruction of signals/images is the well-known Shannon’s entropy. In wavelet theory, this is very well known and researchers are familiar with it. In the present work, a step forward is proposed based on more general wavelet tools. New approach is proposed for the reconstruction of signals/images provided with multiwavelets Shannon-type entropy to evaluate the order/disorder of the reconstructed signals/images. Efficiency and accuracy of the approach is confirmed by a simulation study on several models such as ECG, EEG and DNA/Proteins’ signals.
Non-rigid medical image registration using image field in Demons algorithm

In medical imaging application, non-rigid registration is an important step that requires both accuracy and efficiency to align the deformed query image with the reference image. For non-rigid registration, Demons is generally used as an effective algorithm. However, since the computational model of Demons uses only the gradient information, the directional information of the image has not been fully utilized. In this paper, we proposed a novel non-rigid registration method for processing medical images by introducing the image field to the Demons algorithm. As direction information is very important for spatial transformation in registration and the image fields contain the direction of the image, by combing image fields with the traditional model based registration algorithm, we introduce the orientation field directly to the model for estimating the distortion, and thus make a better use of the direction information of images and simplify the deformation model. Different performance measures are used to evaluate the qualitative measures of the proposed scheme. Experiments on brain images and fundus images show the improvement of the proposed algorithm compared to the other existing state-of-the-art methods.
TCN: Transferable Coupled Network for Cross-Resolution Face Recognition

Cross-resolution face recognition (CRFR) aims to learn the matching of a low-resolution (LR) probe image with a database of high-resolution (HR) gallery images. Existing methods including super resolution and projection-based algorithms are not recognition-oriented and computationally expensive, or ignore the inter-class associations across resolutions. To address the issues, we propose a novel end-to-end Transferable Coupled Network (TCN) for CRFR. Specifically, the TCN consists of two networks for the HR and LR domains, respectively. To reduce the resolution mismatch, a transferrable triple loss (TTL) is introduced to pull together cross-resolution positive pairs (intra-class) and also enforce margins towards negative ones (inter-class) from both domains. Besides, to keep stability and faster convergence, a novel online triplet selection method is proposed. Empirically, the proposed TCN model consistently outperforms the state-of-the-art methods among various low resolutions and architectures on public LFW and SCFace benchmarks.
Reflection symmetry aware image retargeting

Reflection symmetry is one of the most commonly occurring and prominent visual attributes present in the real world. With an increase in the display devices of different sizes and aspect ratios, the images captured from the real world need to be resized to fit to the display device. In this paper, we propose a novel image retargeting approach which preserves the reflection symmetry present in the image during the image retargeting process. We detect the symmetry region present in the image using symmetry axis detection and object proposals. We propose a novel framework for finding an optimized reflected seam for the least energy seam defined by the seam carving approach. The symmetry axis and the symmetric object are preserved by adding or removing a seam and its reflected counterpart together. We show better preservation of symmetry axis, preservation of shape of the symmetric object, and quality of image retargeting when compared to the existing methods using three quantitative measures along with the qualitative results.
Phonemic-level Duration Control Using Attention Alignment for Natural Speech Synthesis

Recent attention-based end-to-end speech synthesis from text systems have achieved human-level performance. However, many approaches cause a sequence-to-sequence model to generate only averaged results of the input text, making it difficult to control the duration of utterance. In this study, we present a novel mechanism for phonemic-level duration control (PDC) in a nearly end-to-end manner in order to solve this problem. We used a teacher attention alignment generated by an annotation speech analyzer program. Our method is inspired by the idea that the duration of a phoneme is highly related to its phonemic features. These phonemic features are saved on the attention alignment by adding duration embedding to it. This enables the model to learn and control the phonemic and rhythmic features of speech. We also show that providing alignment information as a teacher loss term improves training speed and notably, makes the model better at controlling the speed of dramatic change in phonemic-level duration with subjective demonstration. As a result, we show that our PDC speech synthesis with alignment loss outperforms other baseline methods without losing the ability to control the duration of phonemes in extremely adjusted environments with faster convergence.
A PCA‐Based Framework for Determining Remotely Sensed Geological Surface Orientations and Their Statistical Quality

The orientations of planar rock layers are fundamental to our understanding of structural geology and stratigraphy. Remote sensing platforms including satellites, unmanned aerial vehicles, and Light Detection and Ranging scanners are increasingly used to build three-dimensional models of structural features on Earth and other planets. Remotely gathered orientation measurements are straightforward to calculate but subject to uncertainty inherited from input data, differences in viewing geometry, and the plane-fitting process, complicating geological interpretation. Here, we improve upon the present state of the art by developing a generalized means for computing and reporting errors in strike-dip measurements from remotely sensed data. We outline a general framework for representing the error space of uncertain orientations in Cartesian and spherical coordinates and develop a principal component analysis (PCA) regression method, which captures statistical errors independent of viewing geometry and input data structure. We also introduce graphical techniques to visualize the uniqueness and quality of orientation measurements and a process to increase statistical power by jointly fitting bedding planes under the assumption of parallel stratigraphy. These new techniques are validated by comparison of field-gathered orientation measurements with those derived from minimally processed satellite imagery of the San Rafael Swell, Utah, and unmanned aerial vehicle imagery from the Naukluft Mountains, Namibia. We provide software packages supporting planar fitting and visualization of error distributions. This method increases the precision and comparability of structural measurements gathered using a new generation of remote sensing techniques.
Estimation capabilities of biodiesel production from algae oil blend using adaptive neuro-fuzzy inference system (ANFIS)

Biodiesel produced from different raw materials is the most effective way to solve problems related to the fuel crisis and environmental problems. In the present study, the methodology of the adaptive neuro-fuzzy inference system (ANFIS) for the modeling and estimation of a process applied to the production of biodiesel from the blend of algae oil. The Gaussian membership function was applied and studied. The results of ANFIS are compared with the actual results obtained through the experiment using the mean root-mean-square error (RMSE) and the determination coefficient (R-square). The results show an improvement in the prediction, accuracy, and capacity of the ANFIS technique for estimation. The statistical characteristics of RMSE were 0.2179 and R-squared was 0.9998 obtained in training. As the ANFIS offers a good estimation, the modeling quality can be applied to the biodiesel production process as well.
ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding

Electric Network Frequency (ENF) analysis is a promising forensic technique for authenticating digital recordings and detecting tampering within the recordings. The validity of ENF analysis heavily relies on high-quality ENF signals extracted from multimedia recordings. In this paper, we propose an ENF signal extraction method for rolling shutter acquired videos using periodic zero-padding. Our analysis shows that the extracted ENF signals using the proposed method are not distorted and the component with the highest signal-to-noise ratio is located at the intrinsic frequency. The experimental results show that our proposed method can generate more precise ENF signals than those from the state-of-the-art method.