Reviewerly/exHarmony huggingface dataset:
Voice Recognition and Gender Classification in the Context of Native Languages and Lingua Franca

Voice verification and gender classification from voice were carried out in the context of native (mother tongue) languages and lingua franca languages. A total of 3980 voice utterances recorded in English language and 28 native languages were acquired from 520 bilingual subjects in this paper. We first determined the cross linguistic influence of mother tongue by bilingual speakers on the verification performance of voice recognition using English and native languages' gallery and probe sets. Secondly, we employed transfer learning in training four convolutional neural network models for classifying gender from voice, using training and test samples of English language, exclusively; one dominant native language; and a mixture of 28 native languages. Our results do show that mother tongue or first language, intonation variations, language variety in the training or test sets do influence voice verification and gender classification.
Security Issues and Challenges in Cloud Computing: A Mirror Review

The rapid transition toward cloud computing and its services have attracted an enormous number of users who have also raised concerns on critical security issues. The evolution of new technologies such as IoT and Big data has increased the risk data breach. Security and privacy are major concerns when it comes to data. Various privacy and security concerns related to data security were discussed in this paper. It is also discussed, what are the various countermeasures suggested to secure the data. It emphasizes that even though there are many security models were proposed for better security, but still there is no single solution available to handle all the security and privacy concerns. The future research should focus on providing a solution for prime security issues and a secure model has to be developed.
Audio-Textual Arabic Dialect Identification for Opinion Mining Videos

This study presents an approach to detect Arabic dialects from opinion videos. It conducts a four-way multidialect classification task at utterance level from spoken language (audio), written language (text) and their combination. It also analyzes the effect of speaker on detecting dialects through presenting speaker-dependent and speaker-independent approaches. Word embedding based features are used to represent text modality whereas a combination of time-domain and frequency- domain acoustic features are used to represent audio modality. In case of speaker-independent, textual modality achieves significantly better results than audio modality while combining both modalities results in improving the results yielding F1 score of 63.61%. For speaker-dependent approach, similar performance is achieved for individual audio and text modalities while the best performance is achieved when combining both modalities with F1 score of 85.52%.
A Comparative Assessment of Data Mining Algorithms to Predict Fraudulent Firms

The process of data mining is helpful in discovering meaningful patterns in historical or unstructured data in order to make better business decisions. It helps in creating a better marketing strategy and also helps in risk management, fraud detection, etc. In this study, we put forward a comparative analysis of data mining models for fraud detection. The goal of the analysis is to find the best model which gives high accuracy and is less compute-intensive. We have implemented Decision Trees, Linear Support Vector Machines, RBF Kernel Support Vector Machines, K-Nearest Neighbor, Artificial Neural Network and logistic regression classification models. Further, we have implemented PCA and Ensemble techniques to improve the accuracy of the model and decrease the computational complexity of the models.
Flat and hierarchical user profile clustering in an e-commerce recommender system

Recommender systems are more and more used in different domains of computer science. The collaborative filtering remains a highly prized recommendation technique used by the e-services on the internet. This technique is mainly based on deducing a part of the user interests from the preferences of other users with similar profiles. Among the different approaches, the clustering technique is used to implement collaborative filtering. We propose in this work a comparison between hierarchical and flat user profile clustering based on a case study. The proposed approach is implemented basing on a dataset of user profiles in an e-commerce context.
Need to Know About Combined Technologies of BlockChain and Machine Learning

Blockchain was started by introduction of bitcoin through the revolution of money trade. A lot of alternative applications are emerged by blockchain, such as healthcare management, offer chain management, drug tracing and agriculture, and so on. Recent trend and advance technology are a combination of technologies such as artificial intelligence and machine learning combination (AI/ML). Some witnessing aspects of these two combined technologies are self-driving cars, natural language processing and its methods, and big data. In this paper, we explain about combining these technologies and how they are used in various applications in different ways. These are revolutionizing the modern industries by models and by user experiences and behaviors.
Recurrent Neural Network Based Feature Selection for High Dimensional and Low Sample Size Micro-array Data

Analyzing micro-array data faces many challenges such as high dimension, low sample size and sparse data. Feature selection is a technique to select more relevant features to implement dimension reduction to mitigate these challenges. In this paper, we propose a novel framework of feature selection based on recurrent neural network (RNN) to select a subset of features. Specifically, the proposed framework has been applied to select features from micro-array data for cell classification. We implement four feature selection models with different architectures of recurrent neural networks under the proposed framework, where these architectures include gated recurrent unit (GRU), long short-term memory (LSTM), RNN and bi-directional LSTM. The advantages of the proposed framework is demonstrated via real-world micro-array datasets.
GAN-based Abnormal Detection by Recognizing Ungeneratable Patterns

One of the approaches of image anomaly detection is to build a model to generate normal images and distinguish ungeneratable images as abnormal. Recently multiple studies reported the effectiveness of applying generative adversarial networks (GANs) in the task. A GAN trained on normal images is unable to generate abnormal image which is not included in the manifold of normal images, hence the generated image shows a substantial difference from the original images. Most of the previous studies measure the difference by pixel-wise residual loss, where the essential difference between normal and abnormal is often smeared out by inevitable pixel-level reconstruction error of Generator. In this report, a new GAN-based semi-supervised anomaly detection model, AnnoGAN, is proposed, in which the pixel-wise residual loss is replaced by a classifier network that is trained to recognize the essential difference between normal and abnormal. Proposed model achieves state-of-the-art performance in image anomaly detection using public datasets.
Word Embedding by Combining Resources and Integrating Techniques

In a typical text mining problem, the distribution of the domain-specific repository data does not fully capture all the problem's concepts in the real-world. This issue, termed inadequacy of knowledge, decreases the accuracy of generated models. One aspect of inadequacy of knowledge is the out-of-vocabulary problem, i.e. when a word does not appear in the repository data. In this paper, the out-of-vocabulary issue in GloVe is addressed by changing the form of fed training data, i.e. the n-grams of each word are substituted for the word. This version of GloVe is called here C-GloVe. It is shown that the accuracy of the generated models by C-GloVe is mostly higher than when GloVe or FastText is used, especially for smaller training sets. Also, the issue of inadequacy of knowledge is addressed by proposing a method to integrate local (i.e. domain specific) and universal sources of knowledge and to combine different word embedding algorithms. Our experimental results on three different tasks show that the proposed methods yield higher performance than a standalone source of knowledge and a standalone word embedding algorithm, especially if one algorithm of the combination is trained on the local source and another on the universal source of knowledge. Also, experimental results on the classification task show that the proposed method obtained the same or higher F1-score than BERT in four out of five classification problems.
Sketch based image retrieval using grid approach on large scale database

Several approaches are found in study for image retrieval from the database. A sketch based system is an efficient method which does not require the user to have a high skill to draw the query sketch. Sketch-to-image matching system on a large-scale image database should ensure that the system always tries to find correct matches for a given sketch query. In this paper, we present an efficient method for image retrieval from large database using sketch as a query image. Features are extracted from the image by placing a non-overlapping grid and overlapping grid over the image, respectively. Using a weighted similarity approach, the most similar images to the query are then retrieved from the image dataset. For performing experiments images of flickr15k dataset are used. The proposed method shows excellent retrieval performance in comparison to methods available in the literature.
Computationally efficient and secure anonymous authentication scheme for IoT‐based mobile pay‐TV systems

Abstract In the next few years, the mobile pay‐TV systems will be very popular due to their extensive applications. Providing security and privacy are the most challenging issues in the secure development of mobile pay‐TV systems. To avoid unauthorized access to mobile pay‐TV services, it is very important to authenticate the mobile users and the head end system (HES) in an anonymous manner. Even though several authentication schemes were proposed to provide anonymous authentication, the previously proposed schemes are not fit for mobile pay‐TV applications due to their high computational complexity. Hence, a computationally efficient anonymous authentication scheme is proposed in this article for secure service provision in mobile pay‐TV systems. The proposed authentication scheme can effectively authenticate both the mobile users and the HES with low computational cost in an anonymous manner. In addition, an anonymous batch authentication scheme is also proposed in this article to authenticate a batch of users in the subscription phase to alleviate the authentication burden of the HES. The security analysis section shows that the proposed scheme is more efficient in terms of security and the performance analysis section shows the strength of this article in terms of computational cost, while performing anonymous authentication in mobile pay‐TV systems.
Computational approach to body mass index estimation from dressed people in 3D space

Body mass index (BMI) defines as a person's weight divided by the square of height (BMI ), which is an important indicator of the health condition. The authors study BMI estimation from the three-dimensional (3D) visual data by measuring the correlation between the estimated body volume and BMIs, and then develop an efficient BMI computation method. Their approach consists of body weight and height estimation from normally dressed people in 3D space. To address the influence of loose clothes on body volume estimation, two clothes models are developed to make the volume estimation more accurate. A new RGB-D video dataset is collected for this study, and the reconstructed 3D data are provided by the KinectFusion on depth data. Experimental results show the effectiveness of the approach to work on normal conditions of dressed people. The mean absolute error of the estimated BMI can achieve 2.54 in their experiments.
Schmidt-mode analysis of quadrature entanglement in superpositions of two-mode multiphoton states

The Schmidt-decomposition formalism is proposed to be used for evaluation of the degree of quadrature entanglement in two-mode multiphoton states.
Content Based Medical Image Retrieval Using Multi-Trend Structure Descriptor and Fuzzy k-NN Classifier

In this work, Multi-trend structure descriptor is suggested for retrieving images from heterogeneous medical image dataset. The multi-trend structure descriptors acquires structures in the form of large, small and equal trends along four directions at both global and local level. For the retrieval of grayscale medical images, only the structure information computed by Multi-trend structure descriptor from the texture and edge information of an image is used while for color medical images the color information acquired by Multi-trend structure descriptor is also taken in to consideration for attaining effective retrieval rate. The fuzzy k-NN approach is incorporated to classify the feature vectors and it enhances the retrieval accuracy as well it increases the speed of retrieval. The results obviously shown that the fusion of Multi-trend structure descriptor and fuzzy k-NN outperforms the state-of-the-art approaches.
Subsampled Information Criteria for Bayesian Model Selection in the Big Data Setting

Bayesian methods face unprecedented challenges in the era of big data, as the evaluation of likelihood in each iteration is computationally intensive. To deal with this bottleneck, recent literature focus mostly on speeding up Markov chain Monte Carlo (MCMC). Model selection, which is an important topic, has not received much attention. In the Bayesian context, deviance-based criteria, such as the deviance information criterion (DIC), are well-known for model selection purposes. In this article, we introduce the subsampled DIC and the subsampled information criterion IC in the big data context. Extensive simulation studies are conducted to evaluate the empirical performance of the proposed criterion. The usage of our proposed criterion is further illustrated with an analysis of the Covertype dataset.
Distribution Based Feature Mapping for Classifying Count Data

In this paper, we propose a statistically flexible feature mapping technique for count data which are very common in data analysis and pattern recognition applications. In particular, we are interested in supervised learning to improve the existing non-linear classification techniques using Support Vector Machine (SVM). Perfect representation of the data through a kernel function is strongly dependent on the structure of the data. Thus, choosing an appropriate kernel function or feature mapping technique is necessary to get higher accuracy and in this paper, we address this problem by proposing a new feature mapping function derived from Dirichlet Multinomial and Generalized Dirichlet Multinomial distributions for count data. In order to determine the parameters of the distributions, two approaches namely Expectation Maximization (EM) and Minorization-Maximization (MM) are employed. We evaluate our model by experimenting on two different classification tasks concerning natural scene recognition from images and human action recognition from videos. The results show that, incorporating proposed feature mapping technique with different kernels comparatively gives better results than the base kernels in different classification tasks.
Three-stream fusion network for first-person interaction recognition

First-person interaction recognition is a challenging task because of unstable video conditions resulting from the camera wearer’s movement. For human interaction recognition from a first-person viewpoint, this paper proposes a three-stream fusion network with two main parts: three-stream architecture and three-stream correlation fusion. The three-stream architecture captures the characteristics of the target appearance, target motion, and camera ego-motion. Meanwhile the three-stream correlation fusion combines the feature map of each of the three streams to consider the correlations among the target appearance, target motion, and camera ego-motion. The fused feature vector is robust to the camera movement and compensates for the noise of the camera ego-motion. Short-term intervals are modeled using the fused feature vector, and a long short-term memory (LSTM) model considers the temporal dynamics of the video. We evaluated the proposed method on two public benchmark datasets to validate the effectiveness of our approach. The experimental results show that the proposed fusion method successfully generated a discriminative feature vector, and our network outperformed all competing activity recognition methods in first-person videos where considerable camera ego-motion occurs.
Handwritten Digit String Recognition for Indian Scripts

In many documents digits/numerals may touch each other and hence digit string recognition is necessary as segmentation of individual numeral from the touching string is difficult. In this paper, we propose a digit string recognition system for four Indian popular scripts. Here we consider strings of Kannada, Oriya, Tamil and Telugu scripts for our experiment. This paper has two contributions: (i) we have developed 4 datasets of digit string for each of these four scripts. Each dataset has 20000 numeral string samples for training and 30000 samples for testing. As there is no such dataset available, it will be helpful to the community (ii) we apply a RNN free CNN (Convolutional Neural Network) and CTC (Connectionist Temporal Classifica-tion) based architecture for numeral string recognition. Unlike normal text string, in string of digits has no contextual information among the digits and hence a digit may be followed by an arbitrary digit in a digit string. Because of such behaviors we apply a CNN and CTC based architecture without RNN for numeral string recognition. We tested our scheme on our different test datasets and results are provided.
Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at https://github.com/lola63/HDE-Python.
Challenges of Big Data Visualization in Internet-of-Things Environments

Big data infers in all processes in our life. But the analysis process of huge data is not enough, the human brain heads for detecting a pattern more efficiently when data is performed visually. Data visualization and data analytics play a significant role in decision making in various sectors. It also opens new opportunities for the ideas for big data visualization domain. An essential challenge in data visualization is a huge amount of data in real time or in static form. This paper discusses the importance of data visualization. This paper illustrates the impact of big data on data visualization. It focuses on the explanation of the essential challenges of big data visualization in the real-time stream.
On the Use of Saddlepoint Approximations in High Dimensional Inference

Inference in high dimensional parameter space poses many challenges. One of these is the possible use of saddlepoint approximations. Motivated by a recent use of the saddlepoint approximation to construct a conditional test, we argue that the precision is questionable. We illustrate this by an example giving a 50% relative error in the calculation of the p-value. A power study of the underlying test reveals a low power in many situations. As an alternative it is suggested to use the likelihood ratio test.
Zero-Shot Code-Switching ASR and TTS with Multilingual Machine Speech Chain

Constructing automatic speech recognition (ASR) and text-to-speech (TTS) for code-switching in a supervised fashion poses a challenge since a large amount of code-switching speech and the corresponding transcription are usually unavailable. The machine speech chain mechanism can be utilized to achieve semi-supervised learning. The framework enables ASR and TTS to assist each other when they receive unpaired data since it allows them to infer the missing pair and optimize the models with reconstruction loss. In this study, we handle multiple language pairs of code-switching by integrating language embeddings into the machine speech chain and investigate whether the model can perform with code-switching language pairs that are never explicitly seen during training. Experimental results reveal that the proposed approach improves the performance of the multilingual code-switching language pairs with which the model was trained and can also perform with unknown code-switching language pairs without directly learning on it.
Systematization of Knowledge on Scalability Aspect of Blockchain Systems

Blockchains has redefined the way software industry’s core mechanisms operate. Advent of blockchains have intrigued the industry and research community by the properties like immutability, reliability and availability it adheres. Since then, community has observed extensive research to make this technology viable and replace the existing computing paradigms. Blockchain technology promises global, immutable, self-governed system of records with no intermediaries. Technology with such properties have strong use cases where a secure audit trail is quintessential. But the performance of blockchains is not at par with the existing industry standards making industry reluctant to surge towards the blockchains. This paper presents a comprehensive analysis on the recent approaches used to enhance the performance of blockchain systems.