Reviewerly/exHarmony huggingface dataset:
Neural Codes to Factor Language in Multilingual Speech Recognition

In the past, we adapted neural network based multilingual acoustic models using language codes. In this work, we study the extracted language codes and the language properties they encode: We use the codes to generate language prototype vectors, which represent the features of a language. Computing distances between prototype vectors shows that languages from the same family have smaller distances. This structure found within the feature representation supports the assumption that language codes do encode language information and not other properties like, e.g. channel characteristics, and in addition providing a richer language representation than the language identity alone.The network architecture of our system is based on a factorized model, which consists of multiple language dependent subnets. While we recently demonstrated that this approach enables multilingual setups to outperform monolingual ones, we here propose further optimizations. We evaluated using a) more language dependent subnets and b) wider BiLSTM layers. Our results indicate that using a larger number of language dependent subnets increases the system performance and renders phonetic pretraining superfluous. In addition, increasing the size of the hidden layers further improved the performance, with the system now outperforming the monolingual baseline by 6.3% relative.
Image Error Concealment Based on Deep Neural Network

In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.
Coupled Ista Network for Multi-modal Image Super-resolution

In this paper, we propose a novel deep neural network architecture for multi-modal image super-resolution (MISR). The architecture is based on a new joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency and to map them to a high-resolution version of one modality. In JMDL, we learn three dictionaries and two transform matrices to combine the modalities. By using the learned model, we then design the network architecture by a coupled unfolding of the iterative shrinkage and thresholding algorithm (ISTA). We finally initialize the parameters of our network with a new optimization strategy. The initialized parameters are demonstrated to effectively decrease the training loss and increase the reconstruction accuracy. The numerical results show that our method outperforms other state-of-the-art methods quantitatively and qualitatively for MISR.
Importance of Analytic Phase of the Speech Signal for Detecting Replay Attacks in Automatic Speaker Verification Systems

In this paper, the importance of analytic phase of the speech signal in automatic speaker verification systems is demonstrated in the context of replay spoof attacks. In order to accurately detect the replay spoof attacks, effective feature representations of speech signals are required to capture the distortion introduced due to the intermediate playback/recording devices, which is convolutive in nature. Since the convolutional distortion in time-domain translates to additive distortion in the phase-domain, we propose to use IFCC features extracted from the analytic phase of the speech signal. The IFCC features contain information from both clean speech and distortion components. The clean speech component has to be subtracted in order to highlight the distortion component introduced by the playback/recording devices. In this work, a dictionary learned from the IFCCs extracted from clean speech data is used to remove the clean speech component. The residual distortion component is used as a feature to build binary classifier for replay spoof detection. The proposed phase-based features delivered a 9% absolute improvement over the baseline system built using magnitude-based CQCC features.
Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding

Scalable lossless video coding is an important aspect for many professional applications. Wavelet-based video coding decomposes an input sequence into a lowpass and a highpass subband by filtering along the temporal axis. The lowpass subband can be used for previewing purposes, while the highpass subband provides the residual content for lossless reconstruction of the original sequence. The recursive application of the wavelet transform to the lowpass subband of the previous stage yields coarser temporal resolutions of the input sequence. This allows for lower bit rates, but also affects the visual quality of the lowpass subband. So far, the number of total decomposition levels is determined for the entire input sequence in advance. However, if the motion in the video sequence is strong or if abrupt scene changes occur, a further decomposition leads to a low-quality lowpass subband. Therefore, we propose a content adaptive wavelet transform, which locally adapts the depth of the decomposition to the content of the input sequence. Thereby, the visual quality of the low-pass subband is increased by up to 10.28 dB compared to a uniform wavelet transform with the same number of total decomposition levels, while the required rate is reduced by 1.06% additionally.
An Improved Approach to Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with image-level labels is of great significance since it alleviates the dependency on dense annotations. However, it is a challenging task as it aims to achieve a mapping from high-level semantics to low-level features. In this work, we propose a three-step method to bridge this gap. First, we rely on the interpretable ability of deep neural networks to generate attention maps with class localization information by back-propagating gradients. Secondly, we employ an off-the-shelf object saliency detector with an iterative erasing strategy to obtain saliency maps with spatial extent information of objects. Finally, we combine these two complementary maps to generate pseudo ground-truth images for the training of the segmentation network. With the help of the pre-trained model on the MS-COCO dataset and a multi-scale fusion method, we obtained mIoU of 62.1% and 63.3% on PASCAL VOC 2012 val and test sets, respectively, achieving new state-of-the-art results for the weakly supervised semantic segmentation task.
Combining Phone Posteriorgrams from Strong and Weak Recognizers for Automatic Speech Assessment of People with Aphasia

This paper presents an investigation on applying automatic speech recognition (ASR) to speech assessment of people with aphasia (PWA). A distinctive characteristic of PWA speech is paraphasia, which refers to frequent occurrence of phonemic errors, unintended words and non-verbal sounds. In view of the wide variety of paraphasias, we propose to view the ASR errors so caused as out-of-vocabulary (OOV) words. Inspired by previous research on OOV detection, paraphasias in PWA speech are captured by comparing the phone posteriorgrams of a strongly constrained speech recognizer and a weakly constrained one. The posteriorgrams also reveal other characteristics of impaired speech, e.g., change of speaking rate, voice abnormality. Siamese and 2-channel convolutional neural network (CNN) models are used for classifying the posteriorgram pairs and predicting the severity of aphasia. Experimental results on a Cantonese database of PWA speech confirm the effectiveness of the proposed methods. The best F1 score attained on binary classification (severe versus mild aphasia) is 0.891.
A Lexical Search Model based on word association norms

This work introduces a lexical search model based on a type of knowledge graphs, namely word association norms. The aim of the search is to retrieve a target word, given the description of a concept, i.e., the query. This differs from traditional information retrieval models were complete documents related to the query are retrieved. Our algorithm looks for the keywords of the definition in a graph, built over a corpus of word association norms for Mexican Spanish, and computes the centrality in order to find the relevant concept. We performed experiments over a corpus of human-definitions in order to evaluate our model. The results are compared with a Boolean information retrieval (IR) model, the BM25 text-retrieval algorithm, an algorithm based on word vectors and an online onomasiological dictionary–OneLook Reverse Dictionary. The experiments show that our lexical search method outperforms the IR models in our study case.
Pre-processing Techniques for Detection of Blurred Images

Blur detection and estimation have progressively became an imminent arena of computer vision.Francis, Leena Mary Along with heightening usage of mobiles and photographs, detecting the blur is purposed over to enhance or to remove the images. PrE-processing Techniques for DEtection of Blurred Images(PET-DEBI) was framed to detect the blurred and undistorted images.Sreenath, N. The frailty of Laplacian has been overcome by Gaussian filter to remove the noise of the image; then, the variance of Laplacian is calculated over the images. Through analysing the variance of the images, appropriate threshold is circumscribed and further used as limitation to define blurred and unblurred images. PET-DEBI was implemented and experimented yielding encouraging results with accuracy of 87.57%, precision of 88.88%, recall of 86.96% and F-measure of 87.91%.
Performance Analysis of Frequency and Graph Theoretic Based Text Summarization

Nowadays people are becoming busy with their office and family activities. They do not get enough time to study the newspaper completely. But they need to update themselves to the events of the current world. Hence, text summarization can help them to know the events happening all over the world very quickly and saves our valuable time. Automatic summarization is a way of bringing that gist from a large paragraph by coding in the computer. In the paper, we have used term frequency and graph theory summarizing techniques where the first uses word frequency while in the second, each sentence is assumed as a node and the resemblance between sentences is assigned to their corresponding edges. Here, we have used python as the programming language for coding. The analysis shows that the graph-theoretic approach provides more accurate result than frequency summarizer although it takes more time.
A Novel Framework for Designing Directional Linear Transforms with Application to Video Compression

Transforms incorporating directional information are appealing in a wide range of applications. In this paper, we introduce a new framework that allows to define a directional transform starting from any two-dimensional separable transform. The proposed method is highly general and it can be of interest in many areas of signal processing. We also show an example of possible application. We define a directional integer DCT and DST and we show their application in video compression by integrating them in the HEVC video coding standard.
An Image Coding Approach Based on Mixture-of-experts Regression Using Epanechnikov Kernel

In this paper, we propose an optimal modeling framework for image compression using EMM (Epanechnikov Mixture Model). Epanechnikov Kernel and its correlated statistics are basement of our Epanechnikov Mixture Regression (EMR). In our scheme, the stochastic processes of the pixel values are modelled as an EMM with K experts in three-dimensional space and then we use EMR to search for the optimal solution, whose parameters are determined through EM (Expectation-Maximization) algorithm. In the process of regression, the conditional density is the regression kernel function. Experimental results show that the proposed scheme is effective especially for the image with complex texture without consuming extra bits compared to Gaussian Mixture Regression (GMR).
Delay-Free Tracking Differentiator Design Based on Variational Mode Decomposition: Application on MEMS Gyroscope Denoising

This paper presents a delay-free tracking differentiator based on variational mode decomposition (VMD) for extracting the useful signal from a noisy measurement of gyroscope. Sigmoid function-based tracking differentiator (STD) is a novel tracking differentiator with the advantages of noise-attenuation ability and dynamical performance. However, there is a contradiction in STD; i.e., selecting a larger acceleration factor may cause faster convergence but bad random noise reduction whereas selecting a smaller acceleration factor may lead to signal delay but effective random noise reduction. Here, multiscale transformation is introduced to overcome the contradiction of STD. VMD is selected to decompose the noisy signal into multiscale components, and the correlation coefficients between each component and original signal are calculated, then the component with biggest correlation coefficient is reserved and other components are filtered by the proposed adaptive STD algorithm based on the correlation coefficient of each component, and finally the denoising result is obtained after reconstruction. The prominent advantages of the proposed algorithm are as follows: (i) compared to traditional tracking differentiators, better noise suppression ability can be achieved with suppression of time delay; (ii) compared to other widely used denoising methods, a simpler structure but better denoising ability can be obtained.
An Improved Faster R-CNN Algorithm for Electric Equipment Detection

More and more images and videos have been collected in power system, however, it is still a challenge to recognize the electric equipment in the unstructured multimedia stream automatically. In this paper, we propose to apply an improved Faster R-CNN algorithm to detect and recognize the electric equipment for the electric equipment images. To be more specific, we adjust the key parameters of the Faster R-CNN framework to detect and recognize the electric equipment simultaneously. In order to improve the recognition accuracy, we propose DisturbIoU algorithm to prevent the network training from over-fitting. Experiment results show that for insulator, transformer and power pole, the average recognition accuracy rate of the proposed algorithm is 96.33%, 99.89%, 97.98% respectively, which is 3.28%, 6.02% and 6.21% higher than that of the traditional Faster R-CNN algorithm.
A Procedure Model for Situational Reference Model Mining.

This contribution introduces the concept of Situational Reference Model Mining, i. e., the idea that automatically derived reference models, although derived from identical input data, are intended for different purposes and therefore have to meet different requirements. These requirements determine the reference model character and thus the technique that is best suited for mining it. Situational Reference Model Mining is based on well-known design principles for reference modeling, such as configuration, aggregation, specialization, instantiation, and analogy. We present a procedure model for Situational Reference Model Mining and demonstrate its usefulness by means of a case study. Existing techniques for Reference Model Mining are examined and mapped to their underlying design principles. Our approach provides reference model designers with first guidelines regarding their choice of mining technique and points out research gaps for the development of new approaches to reference model mining.
Improving the QoE of DASH over SDN: A MCDM Method with an Intelligent Approach

As being one of the most popular applications in the last decade, dynamic adaptive video streaming applications are used by Internet users every day. In such applications, the underlying architecture allows users to change quality adaptively as their request. The purpose of quality or rate adaptation algorithm is to achieve highest QoE possible. In this paper, we propose a rate adaptation algorithm which allows to increase the quality of already buffered video by using Multi-Criteria Decision Making (MCDM) method. Increasing the quality of the buffered video can be beneficial in areas from resiliency to entertainment. We propose to utilize SDN for deciding weights of MCDM method. For this purpose, SDN controller runs a machine learning algorithm by using its knowledge about current network conditions as an input of the learning algorithm. Simulation results show that users achieve higher QoE by using our approach when compared to conventional rate adaptation algorithm.
Applications of Gaussian Process Latent Variable Models in Finance

Estimating covariances between financial assets plays an important role in risk management. In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear extension of standard factor models with readily interpretable parameters reminiscent of market betas. Furthermore, our Bayesian treatment naturally shrinks the sample covariance matrix towards a more structured matrix given by the prior and thereby systematically reduces estimation errors. Finally, we discuss some financial applications of the GP-LVM.
Improvement of fringe quality for phase extraction in double digital fringe projection

A procedure to improve the quality of the extracted phase by a passband filter within the spatial frequency domain is proposed, using the double-digital fringe projection method for obtaining the contour of the surface of different objects. This method requires the digital projection of two fringe patterns to the same object to generate an interference pattern containing moiré fringes, which are related to the shape of the measured object. The proposed method pretends to remove remnant frequencies of the projected fringes keeping only the moiré pattern.
Comparative Study of Tiger Identification Using Template Matching Approach based on Edge Patterns

Abstract The presence of tigers in residential areas as predators was a threat to humans and their livestock. Through digital image processing, these animals could be identified by matching line patterns on the skin as a characteristic of the tiger using the template matching method, namely comparing the similarities between the lines pattern in the reference image with the main image. However, the presence of plant leaves and shrubs with various shapes could disguise tiger skin line patterns so that it could affect the accuracy of identification results. In this paper, we conducted a comparative study to identify tigers based on edge lines which were then processed using three edge detection method namely Canny, Laplacian and Sobel. The results of each operator were further processed using template matching algorithm to get the accuracy of object identification (tiger).
Wide or Narrow? A Visual Attention Inspired Model for View-Type Classification

Emerging research revealed that the view-type of photos is not only related to the field of data sciences, such as the sentiment brought forth by sightseeing spots, but also in the field of social sciences about human emotions and behaviors. These potential usages of view-types trigger a challenging problem, that is to automatically distinguish them into wide or narrow. In this paper, we present a computational model to classify them inspired by the human visual system. We found two cues that can represent the visual attention, i.e., focus cue and scale cue. The focus cue is modeled in the frequency domain using the non-sampled contourlet transform (NSCT) and speeded up robust features (SURF). The scale cue is modeled by defining the spatial size and conceptual sizes of an object in the image, whereby AdobeBING and convolutional neural network are used for the respective measurements. By integrating these focus and scale models, a robust scheme is hence proposed for this non-trivial task. The experiments on a newly established dataset, which has 5050 natural images, show better performance by our proposal when compared to the state-of-the-arts.
Dress me up!

It is sometimes difficult for a person to find a matching outfit in a clothing store. It would be beneficial if there is a mechanism which allows a person to provide an example of clothes that they are looking for in the form of an image instead of text description and they will be provided with similar items that the store have. In this paper, we propose a framework for content-based clothing image retrieval that allows searching and retrieving of similar clothes based on colour and shape features. We first convert the RGB image to HSV colour space. Colour histogram is then computed for colour representation while the shape representation is obtained from low and high thresholding computation. The similarity between input image and image collection is computed based on Manhattan distance. The proposed framework can aid users in finding the desired and similar clothing images from catalogues effectively without the needs of text-based input. Users can also search for similar clothing based on specific features like colour or shape. For each clothes, the item's descriptions like brand, size, and price are also being displayed. We have conducted retrieval effectiveness experiment and questionnaire-based survey to measure the effectiveness of our framework and users' acceptance. We have obtained good results for both evaluation approaches.
Efficient Randomized Defense against Adversarial Attacks in Deep Convolutional Neural Networks

Despite their well-documented learning capabilities in clean environments, deep convolutional neural networks (CNNs) are extremely fragile in adversarial settings, where carefully crafted perturbations created by an attacker can easily disrupt the task at hand. Numerous methods have been proposed for designing effective attacks, while the design of effective defense schemes is still an open area. This work leverages randomization-based defense schemes to introduce a sampling mechanism for strong and efficient defense. To this end, sampling is proposed to take place over the matricized mid-layer data in the neural network, and the sampling probabilities are systematically obtained via variance minimization. The proposed defense only requires adding sampling blocks to the network in the inference phase without extra overhead in the training. In addition, it can be utilized on any pre-trained network without altering the weights. Numerical tests corroborate the improved defense against various attack schemes in comparison with state-of-the-art randomized defenses.