Reviewerly/exHarmony huggingface dataset:
GRU-SVM Model for Synthetic Speech Detection

Voice conversion and speech synthesis techniques present a threat to current automatic speaker verification systems. Therefore, to prevent such spoofing attack, choosing an appropriate classifier for learning relevant information from speech feature is an important issue. In this paper, a GRU-SVM model for synthetic speech detection is proposed. The Gate Recurrent Unit (GRU) neural network is considered to learn the feature. The GRU can overcome the problems of gradients vanishing and explosion in traditional Recurrent Neural Networks (RNN) when learning the temporal dependencies. The Support Vector Machines (SVM) plays a role in regression before softmax layer for classification. An excellent performance after the SVM regression has shown in the case of classification ability and data gradient descent. We also obtain the optimal speech feature extraction method and apply it to the classifier for training by a large amount of verification and analysis. Experimental results show that the proposed GRU-SVM models gain higher prediction accuracy on data sets, and an average detection rate of 99.63% has been achieved in our development database. In addition, the proposed method can improve the learning ability of the model effectively.
Clustering with varying risks of false assignments in discrete latent variable model

In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases.
An Improved Action Recognition Network Based on Appearance and Relation

With the increase of people's security awareness, people need to distinguish human behavior in videos. In order to avoid manpower consumption, action recognition based on deep learning has been widely studied in recent year.3D convolution is used to improve computing efficiency for action recognition. In this paper, an improved action recognition network based on appearance and relation is proposed. The residual block in the ResNet3D network is redesigned into a new residual block with two branches of appearance and relation. In addition, the separable spatial/temporal convolution is used in the relation branch for more spatio-temporal information and less loss. The proposed architecture achieves comparable results to state-of-the-art methods on UCF101 .
A Comparative Study of Classifiers for Extractive Text Summarization

Automatic text summarization (ATS) is a widely used approach. Through the years, various techniques have been implemented to produce the summary. An extractive summary is a traditional mechanism for information extraction, where important sentences are selected which refers to the basic concepts of the article. In this paper, extractive summarization has been considered as a classification problem. Machine learning techniques have been implemented for classification problems in various domains. To solve the summarization problem in this paper, machine learning is taken into consideration, and KNN, random forest, support vector machine, multilayer perceptron, decision tree and logistic regression algorithm have been implemented on Newsroom dataset.
A Novel Architecture of Pedestrian Detection

In the past decade, the pedestrian detection has drawn much attention due to the significant role it plays in artificial intelligence system and vehicle assisted driving system. In order to achieve a balance between recognition rate and detection time, a novel architecture of pedestrian detection has been proposed in this paper. Firstly, we get the enhanced HOG feature (eHOG) by enhancing Histograms of Oriented Gradients feature contrast. Then, the eHOG is used as an input of XGBoost to recognize pedestrian/non-pedestrian. The architecture proposed is tested on MIT and INRIA pedestrian datasets, experimental results show that recognition rate can reach 91.13% on MIT dataset and 99.11% on INRIA dataset, and testify the effect of our architecture proposed.
A Novel Graph Theoretic Image Segmentation Technique

In this paper, a novel graph theoretic image segmentation technique is proposed, which utilizes forest concept for clustering. The core idea is to obtain a forest from the image followed by construction of average value super pixels. Thereafter, a merging criterion is proposed to merge these super pixels into two big classes thereby binarizing and thresholding the image separating background from foreground. Extensive experimentation and comparative analysis are finally performed on a diverse set of images to validate the technique and have noted the significant improvements.
Gradient Boosting Machine and LSTM Network for Online Harassment Detection and Categorization in Social Media

We present a solution submitted to the Social Media and Harassment Competition held in collaboration with ECML PKDD 2019 Conference. The dataset used is as set of tweets and the first task was on the detection of harassment tweets. To deal with this problem, we proposed a solution based on a gradient tree-boosting algorithm. The second task was categorization harassment tweets according to the type of harassment, a multiclass classification problem. For this problem we proposed a LSTM network model. The solutions proposed for these tasks presented good predictive accuracy.
FKNDT: A Flexible Kernel by Negotiating Between Data-dependent Kernel Learning and Task-dependent Kernel Learning

Kernel learning is a challenging issue which has been vastly investigated over the last decades. The performance of kernel-based methods broadly relies on selecting an appropriate kernel. In machine learning community, a fundamental problem is how to model a suitable kernel. The traditional kernels, e.g., Gaussian kernel and polynomial kernel, are not adequately flexible to employ the information of the given data. Classical kernels are unable to sufficiently depict the characteristics of data similarities. To alleviate this problem, this paper presents a Flexible Kernel by Negotiating between Data-dependent kernel learning and Task-dependent kernel learning termed as FKNDT. Our method learns a suitable kernel by way of the Hadamard product of two types of kernels; a data-dependent kernel and a set of pre-specified classical kernels as a task-dependent kernel. We evaluate a flexible kernel in a supervised manner via Support Vector Machines (SVM). We model a learning process as a joint optimization problem including data-dependent kernel matrix learning, multiple kernel learning by means of quadratic programming, and standard SVM optimization. The experimental results demonstrate our technique provides a more effective kernel than the traditional kernels. Our method is better than other state-of-the-art kernel-based algorithms in terms of classification accuracy on fifteen benchmark datasets.
Resources and competencies as major determinants of university models 4.0

The article considers the concept of resources and competences in the context of the transition of universities to a new generation model - “University 4.0.” Based on an analysis of the historical variability of university models, it is justified that resources and competencies act as the main determinants of the 4.0 university model in the modern realities of higher education. The analysis of sources claims that the movement from University 1.0 to University 4.0 increases the level of “transition” of talent and knowledge. In this way, the authors reach to the concept of “resources,” as supra-competent determinants of the growth of the university. Based on the analysis of the global challenges of the modern world, the need to distinguish the spiritual mission of universities as important actors in the development of modern society is justified. Is presented the model of the University of the Fourth Generation, developed by Al-Farabi KazNU.
Adjoint Dynamics of Stable Limit Cycle Neural Networks

Exploding and vanishing gradient are both major problems often faced when an artificial neural network is trained with gradient descent. Inspired by the ubiquity and robustness of nonlinear oscillations in biological neural systems, we investigate the properties of their artificial counterpart, the stable limit cycle neural networks. Using a continuous time dynamical system interpretation of neural networks and backpropagation, we show that stable limit cycle neural networks have non-exploding gradients, and at least one effective nonvanishing gradient dimension. We conjecture that limit cycles can support the learning of long temporal dependence in both biological and artificial neural networks.
An Improved Fast Correlation Attack on Fruit-80

In recent years, small-state stream ciphers (SSCs) has drawn serious attention, whose proposal is to reduce the internal state without harming its security against time-memory-data (TMD) tradeoff attacks. The idea of it allows designing stream ciphers with small area size and low power consumption. It is regarded as a promising low-cost solution for IoT security. A small-state stream cipher is highly efficient in terms of hardware cost. Compared to conventional designs, the most significant advantage of the proposed designs is that they are highly efficient in terms of hardware cost. Based on the idea, the first small-state stream cipher was proposed in 2015, named Sprout. But soon it was proved to be insecure. However, its design shows a hopeful direction of designing stream ciphers. Then, more small-state stream ciphers are proposed. Unfortunately, the lack of enough theoretical work towards small-state stream cipher apparently restricts its development. This motivates researchers to study the security of these small-state stream ciphers. Fruit-80 is the final version of Grain-like small-state stream cipher proposed in 2018. In this paper, by combining the fast correlation attack algorithms for small-state stream ciphers with a technique-elimination by factorization, we present a fast correlation attack (FCA) on Fruit-80 with the data complexity $$ 2^{23.35} $$ and the time complexity $$ 2^{72.71} $$. It can be seen from the analysis that the design is insecure and we need further study.
A dilated CNN model for wide-band remote sensing image classification

In this paper, a model of dilated convolutional neural network (CNN) is proposed. Dilated convolution is used to instead the convolution layer in the traditional CNN and to realize a faster and more accurate image classification model. In the experiment, the remote sensing image of the earth's terrain, including ocean, desert and city et al. taken by the wideband imaging spectrometer onboard Tiangong-2 was used as the training and testing set to record the accuracy and training time, and compared with the traditional CNN. The results show that the dilated CNN model performs well in the wide-band remote sensing image data set and achieves better accuracy with less computational resources consumed.
Online optical inspection of air permeability of packaging materials during laser perforation

Laser perforations are formed in packaging to control the rate of gas exchange and maintain a balance between the respiration of the product and the permeation of the package. The air permeability of the packaging materials is typically measured using air-driven methods, but these do not allow for online measurement during the rapid production of perforations. The required permeability is generally obtained through trial and error. This paper describes the simulation of air-driven methods through computational fluid dynamics to study the effects of turbulence on the rate of air flow through perforations. A mathematical model is developed to quantify the influence of the microperforated size, the total area of exchange, and the thickness of the material on air permeability under certain temperature and pressure differences. The numerical results are in good agreement with the experimental results. An optical system based on the model is designed for the online inspection of packaging materials during the rapid production of laser perforations. Air permeability is precisely determined by analyzing continuous images of the perforations. Perforation characteristics such as shape, quantity, and density can also be measured to allow for feedback control of the perforation process according to the features of the packaged products. The validity and accuracy of the developed system are experimentally verified.
A Method to Generate Synthetically Warped Document Image

The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way.
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CSIDH is an isogeny-based key exchange protocol proposed by Castryck et al. A secret key of CSIDH is an integer vector that is sampled uniformly from an L∞-norm ball. A public key of CSIDH is generated by calculating the action of an ideal class corresponding to a secret key. If the computation of the action is accelerated, CSIDH becomes faster. Since the computational cost depends on the value of a secret key, just changing the secret key space may give a faster key exchange. Indeed, several papers showed that CSIDH becomes faster when a secret key space is changed to a weighted L∞-norm ball. In this paper, we revisit the approach and find an optimal secret key space that minimizes the average computational cost of the group action. At first, we analyze the computational cost of CSIDH for every integer vector as a secret key and obtain an optimal secret key space. Since the optimal key space is too complicated to sample a secret key uniformly, we approximate the optimal key space with an L1-norm ball and propose algorithms for uniform sampling. By experiment, we show that the average computational cost of the L1-norm ball is reduced by about 20% compared to that of the L∞-norm ball, using a precomputed table of 160 Kbytes. Furthermore, the cost is only 1.08 times as large as that of the cost of the optimal secret key space.
Detecting Devices and Protocols on VPN-Encrypted Networks

Information assurance properties are fundamental in securing emerging computer systems. Maintaining properties like authorization in these systems relies on knowing the protocol being used and the type of device using it. Scenarios like IoT often include a diverse set of device types and protocols which call for an approach that can encompass this diversity, such as network traffic analysis. With encrypted communication becoming more standard, current traffic analysis approaches are rendered ineffective and new means are called for to enable this type of detection. Presented here is a machine learning approach to network analysis that aims to uphold security properties on the network through the fundamental steps of detecting device types and protocols used. By inspecting VPN traffic, we classify different device types as they login with the Open Authorization (OAuth) protocol, achieving 96% correct classification in some scenarios. We then turn our attention to detecting the underlying protocols in a VPN stream, showing a 94.9% correct detection of OAuth. Through these two classification attempts, we show how to overcome specific challenges of machine learning on VPN data such as generating samples and labeling of data.
A Comparative Study of Big Data Tools and Deployment PIatforms

Big data now a days this term becomes not unusual in it industries. As there is a massive quantity of records lies in the industry however there is nothing before massive records comes into image. An example of huge information is probably petabytes (1,024terabytes) or Exabyte's (1,024 petabytes) of information comprising of billions to trillions of facts of millions of people-all from modified causes (e.g. Internet, sales, client touch center, social media, cellular records and so on). The aim of this paper is to advise an optimized huge information platform implementation model through s/w configuration supported open supply.
Mid-Air Gestures for Multi-Fingered Virtual Assembly System with Leap Motion

With the development of human-computer interaction and virtual reality technology, virtual assembly shows its advantages in the fields of robotic and mechanical assembly. However, the traditional virtual assembly is based on the mouse and keyboard devices to interact with the virtual environment with low interactivity and poor sense of reality. This paper proposed a novel approach for designing and implementing a virtual assembly training system based on multi-fingered mid-air gestures with Leap Motion controller. The procedures included the construction of assembly scene, the functional identification of the mid-air gestures and the design of the assembly process. This framework is validated through a sample assembly process of a single-stage gear reducer. Experimental results indicated that the system works well to implement assembly operations with immersive experience and high gesture recognition accuracy. From the results, it can be seen that the proposed system has the potential to be a useful tool for mechanical assembly education and industrial design and manufacturing.
Lossless Multi-component Image Compression Based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks

This work extends the single component Wavelet Prediction Compression (WPC) framework proposed earlier to lossless compression of multi-component imagery. WPC employs a series of convolutional neural networks (CNNs) to generate predictions of wavelet detail subbands, using subbands within the same discrete wavelet transform (DWT) decomposition level. Prediction residuals are then coded in place of original coefficients in the compressed codestream. At the decoder, predictions are reproduced using an identical set of CNNs, and combined with residuals to achieve perfect subband (and image) reconstruction. The proposed Multi-Component Wavelet Prediction Compression (MCWPC) framework extends WPC to multi-component by considering both inter-and intra-component redundancies, resulting in improved spatial and spectral decorrelation of DWT coefficients. Compression results (Table 1) show MCWPC achieves 7.2% and 23.8% bit-rate reductions over JPEG2000 in the YUV and RGB colorspaces, respectively.
Application of Chaos Theory in Image Encryption

Abstract In order to protect the digital image, people have come up with many methods, digital image encryption is one of the most important means. Based on the features of chaotic mapping and the strong correlation between adjacent pixels of images, an image encryption algorithm inspired by chaos theory is proposed. In the specific operation process, chaotic mapping is used to perform chaotic encryption on the displaced image using. The image encryption algorithm is easy to operate, safe and stable, and has been confirmed by many successful examples.
The Design of Automatic Correction System for Spoken English Pronunciation Errors Based on Phonemes

The existing automatic correction system of oral English pronunciation errors has the problem of poor correction effect, in order to solve these problems, an automatic correction system based on phoneme is designed. The hardware design of the system includes architecture, audio signal modulator, demodulator, audio collector and controller, The software design includes audio acquisition of spoken English pronunciation, speech signal matching filter processing, oral pronunciation error detection and oral pronunciation error correction. Through the design of hardware and software of the system, the automatic correction system of spoken English pronunciation errors based on phonemes is realized. The experimental results show that: compared with the existing automatic correction system of spoken English pronunciation errors, the automatic correction system of spoken English pronunciation errors designed in this paper greatly improves the correction effect, which fully shows that the designed system has better application performance.
Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

Increasing malicious users have sought practices to leverage 3D printing technology to produce unlawful tools in criminal activities. It is of vital importance to enable 3D printers to identify the objects to be printed and terminate at early stage if illegal objects are identified. Deep learning yields significant rises in performance in the object recognition tasks. However, the lack of large-scale databases in 3D printing domain stalls the advancement of automatic illegal weapon recognition. This paper presents a new 3D printing image database, namely C3PO, which compromises two subsets for the different system working scenarios. We extract images from the numerical control programming code files of 22 3D models, and then categorize the images into 10 distinct labels. These two sets are designed for identifying: (i). printing knowledge source (G-code) at beginning of manufacturing, (ii). printing procedure during manufacturing. Importantly, we demonstrate that the weapons can be recognized in either scenario using deep learning based approaches using our proposed database. The quantitative results are promising, and the future exploration of the database and the crime prevention in 3D printing are demanding tasks.
Dual and Axiomatic Systems for Constructive S4, a Formally Verified Equivalence

We present a proof of the equivalence between two deductive systems for the constructive modal logic S4. On one side, an axiomatic characterization inspired by Hakli and Negri's Hilbert-style system of derivations from assumptions for modal logic K. On the other side, the judgmental reconstruction given by Pfenning and Davies by means of a so-called dual natural deduction approach that makes a distinction between valid, true and possible formulas. Both systems and the proof of their equivalence are formally verified using the Coq proof assistant.