Reviewerly/exHarmony huggingface dataset:
Implementation of El-Gamal algorithm for speech signals encryption and decryption

In the applications of Internet and wireless communication network, information security is one of the most challenging aspects. Cryptography is the best solution that offers the requisite protection from unintended persons. By using encryption and decryption mechanisms, cryptography can convert the data form its readable form to unreadable one so that only the intended receiver can read the message and alter it. By this way, one can ensure that message can be sent or stored without any modification. The presented work is concerned with a particular type of asymmetric key cryptography called El-Gamal algorithm to be used for speech signals encryption and decryption. First, the speech signal is encrypted by employing El-Gamal algorithm throughout encryption process at the sender and then transmitted to the particular recipient. Second, the recipient will recover the original signal throughout the decryption process. The cryptosystem performance is evaluated via different quality measures for audio signals encryption/decryption including Signal to Noise Ratio, Segmental Signal to Noise-Ratio and Log-Likelihood Ratio that are reach to -35.0224 dB, -38.0201 dB and 2.1145 respectively for the encrypted signal. The given results confirm the effectiveness of the presented scheme and robustness against noise effect.
New insights on Kimberlite emplacement around the Bundelkhand Craton using integrated satellite-based remote sensing, gravity and magnetic data

The present study attempts a conjugate analysis of satellite-based remote sensing, gravity, and magnetic data for evaluation of structures and tectonic for new insights on Kimberlite emplacement around the Bundelkhand Craton. Initially, Landsat Operational Land Imager (OLI) data has been enhanced using high-pass filtering for the delineation of lineaments. A combined lineament density map has been prepared from the processed Landsat image, Bouguer gravity, and RTP (Reduction to pole) magnetic data, which are further correlated with the reported Kimberlite/Lamproite pipes. The depth to the lithospheric Mantle of the study area has been estimated from EIGEN6C4 Bouguer gravity data using the Parker-Oldenburg algorithm. The association of the reported Kimberlite/Lamproite pipes in the study area have been analyzed with the i) composite lineament density map, ii) different mantle blocks of high and low Moho depths, iii) lithospheric mantle density, and iv) tectonic setting. Some new potential zones for Kimberlite/Lamproites emplacement have been identified based on the integrated analysis of the above mentioned parameters.
Why are My Flows Different? A Tutorial on Flow Exporters

Network flows build the basis of modern network data analysis by aggregating properties of network packets with common characteristics. A consistent and unambiguous definition of the network flow concept is an indispensable prerequisite and starting point for reproducible network research. However, in today's practice, the flow output of distinct flow exporters, which is software to generate flows from observed network packets, varies substantially on identical network packet stream input. In this paper we present an in-depth comparison of different flow exporters and show how their outputs differ significantly. We argue that this substantially impairs reproducibility for traffic analysis research. We first present the detailed flow definition of the IP Flow Information eXport (IPFIX) standard including explanations and examples, analyze design and implementation of existing flow exporters, and explore the reasons why many projects and publications chose to implement their own flow exporters. Based on this analysis we highlight the main challenges in the flow exporting process and present a detailed tutorial on how to design and implement a flow exporter such that it yields consistent, reproducible output. Based on the tutorial's theoretical analysis and lessons learned we present design and main concepts of a versatile, flexible, and open source flow exporting solution called go-flows that generates deterministic, reproducible network flows. Finally, we present a flow-by-flow comparison of the analyzed flow exporters' output, explore the differences in terms of their generated flows, compare flow exporter performance, and conclude with guidelines on parameters that play a crucial role in improving the reproducibility of exported flows.
A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics

Topic models have become popular for the analysis of data that consists in a collection of n independent multinomial observations, with parameters N i ∈ N and i ∈ [0, 1] p for i = 1, . . ., n.The model links all cell probabilities, collected in a p × n matrix , via the assumption that can be factorized as the product of two nonnegative matrices A ∈ [0, 1] p×K and W ∈ [0, 1] K×n .Topic models have been originally developed in text mining, when one browses through n documents, based on a dictionary of p words, and covering K topics.In this terminology, the matrix A is called the word-topic matrix, and is the main target of estimation.It can be viewed as a matrix of conditional probabilities, and it is uniquely defined, under appropriate separability assumptions, discussed in detail in this work.Notably, the unique A is required to satisfy what is commonly known as the anchor word assumption, under which A has an unknown number of rows respectively proportional to the canonical basis vectors in R K .The indices of such rows are referred to as anchor words.Recent computationally feasible algorithms, with theoretical guarantees, utilize constructively this assumption by linking the estimation of the set of anchor words with that of estimating the K vertices of a simplex.This crucial step in the estimation of A requires K to be known, and cannot be easily extended to the more realistic set-up when K is unknown.This work takes a different view on anchor word estimation, and on the estimation of A. We propose a new method of estimation in topic models, that is not a variation on the existing simplex finding algorithms, and that estimates K from the observed data.We derive new finite sample minimax lower bounds for the estimation of A, as well as new upper bounds for our proposed estimator.We describe the scenarios where our estimator is minimax adaptive.Our finite sample analysis is valid for any n, N i , p and K, and both p and K are allowed to increase with n, a situation not handled well by previous analyses.We complement our theoretical results with a detailed simulation study.We illustrate that the new algorithm is faster and more accurate than the current ones, although we start out with a computational and theoretical disadvantage of not knowing the correct number of topics K, while we provide the competing methods with the correct value in our simulations.
Determinant Expressions of Constraint Polynomials and the Spectrum of the Asymmetric Quantum Rabi Model

The purpose of this paper is to study the exceptional eigenvalues of the asymmetric quantum Rabi models (AQRM), specifically, to determine the degeneracy of their eigenstates. Here, the Hamiltonian $H^{\epsilon}_{\text{Rabi}}$ of the AQRM is defined by adding the fluctuation term $\epsilon \sigma_x$, with $\sigma_x$ being the Pauli matrix, to the Hamiltonian of the quantum Rabi model, breaking its $\mathbb{Z}_{2}$-symmetry. The spectrum of $H^{\epsilon}_{\text{Rabi}}$ contains a set of exceptional eigenvalues, considered to be remains of the eigenvalues of the uncoupled bosonic mode, which are further classified in two types: Juddian, associated with polynomial eigensolutions, and non-Juddian exceptional. We explicitly describe the constraint relations for allowing the model to have exceptional eigenvalues. By studying these relations we obtain the proof of the conjecture on constraint polynomials previously proposed by the third author. In fact, we prove that the spectrum of the AQRM possesses degeneracies if and only if the parameter $\epsilon$ is a half-integer. Moreover, we show that non-Juddian exceptional eigenvalues do not contribute any degeneracy and we characterize exceptional eigenvalues by representations of $\mathfrak{sl}_2$. Upon these results, we draw the whole picture of the spectrum of the AQRM. Furthermore, generating functions of constraint polynomials from the viewpoint of confluent Heun equations are also discussed.
Data Augmentation Method of Object Detection for Deep Learning in Maritime Image

Maritime object detection is an essential element for the situational awareness in autonomous ships. Recently, as the deep learning technology evolves, the attempt that the ship recognize the surrounding environment by using deep learning technology is gradually increasing. Deep learning methods, however, require a lot of data, but lack a publicly available dataset for object detection in the maritime domain. In this paper, we proposed a data augmentation method that can automatically extend the object detection dataset in maritime image. Extract the mask of the foreground object and combine it with the new background to automatically generate the location information and data of the object. Through the proposed method, we can learn high quality data by configuring various limited data features.
Isolated word language identification system with hybrid features from a deep belief network

Summary The representation of good audio features is the first and foremost requirement for improving the identification performance of any system. Most of the representation learning approaches are based on connectionist systems to learn and extract latent features from the speech data. This research work presents a hybrid feature extraction approach to integrate Mel‐Frequency Cepstral Coefficients (MFCC) features with Shifted Delta Cepstral (SDC) coefficients features, which are further stacked to Deep Belief Network (DBN), for yielding new feature representations of the speech signals. DBN is utilized for unsupervised feature learning on the extracted MFCC‐SDC acoustic features. A 3‐layer Back Propagation Neural Network (BPNN) classifier is initialized in terms of the learning outcomes of hidden layers of DBN for identifying language from the uttered speech. The efficiency of the proposed approach is evaluated by simulating several experimental algorithms on the user‐defined database of isolated words in four languages, namely, Tamil, Malayalam, Hindi, and English, in the working platform of MATLAB. The obtained results for the proposed hybrid approach MFCC‐SDC‐DBN are promising. The proposed approach is also compared with the baseline feature extraction approach MFCC‐SDC by utilizing traditional acoustic features and BPNN classifier. The accuracy obtained with our proposed approach is 98.1% whereas that of the baseline approach is 82%, thereby providing an overall improvement of 16.1%.
The Embodied Gaze: Exploring Applications for Mobile Eye Tracking in the Art Museum

Recent advances in Mobile Eye Tracking (MET) technology facilitate the investigation of visitors' embodied visual behaviors as they move through exhibition spaces. Our MET-based pilot study of visitor behaviors in an art museum demonstrates the value of MET for identifying 'hotspots' of attention. The study also confirms the occurrence of the movement patterns identified by Eghbal-Azar in non-art museums and demonstrates how two patterns—'the Long Gaze' and 'Reading'—can be usefully described in more detail. To illustrate this, we report on one visitor's experience with a single painting, noting the complex embodied visual behaviors associated with gazing and reading. Our findings allow us to reflect on the potential benefits of eye tracking not only for mapping visitor engagement but also for promoting it. In contrast to art museum installations that use static eye tracking as a form of visitor engagement, we argue that MET applications enable visitors to observe, reflect on, and potentially modify, personal viewing practices.
Conditional generative adversarial networks based on the principle of <scp>homologycontinuity</scp> for face aging

Abstract Age is one of the most important biological characteristics of the human face. The increase of age coincides with the increase of the aging degree of the face. Face aging synthesis is attracting increasingly more attention from domestic and overseas scholars in the computer vision and computer graphics fields, and it can be integrated into the basic research of face correlation, such as cross‐age face analysis and age estimation. At present, some achievements have been made in face aging synthesis research; however, it is still an urgent problem to reduce the number of parameters and computational complexity of the network while ensuring the aging effect. Therefore, a new face aging algorithm is proposed in this article. Unlike the previous methods of aging process simulation, we introduce an assisted age classification network based on the principle of homology continuity, which is more in line with the human cognition process. After pretraining, the result of age classification is improved, and the pretraining model is then added to the framework of aging face generation for fine‐tuning to constrain the generated aging face, which can improve the aging accuracy of the generated image. Furthermore, we reconstruct the input face by using the age tag of the input face and the synthesized aging face and maintain the identity invariance in the face aging process by minimizing the reconstruction loss. The experimental results show that the method proposed in this article produces a considerable effect of face aging and significantly reduces the number of parameters and the complexity of computational.
Multilayer Ensemble Evolving Fuzzy Inference System

In order to tackle high dimensional, complex problems, learning models have to go deeper. In this article, a novel multilayer ensemble learning model with first-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on a sample-by-sample basis and self-organizes its multilayered system structure and meta-parameters in a feedforward, noniterative manner. Benefiting from its multilayered distributed representation learning ability, the ensemble system not only demonstrates the state-of-the-art performance on various problems, but also offers high level of system transparency and explainability. Theoretical justifications and experimental investigation show the validity and effectiveness of the proposed concept and general principles.
Framework for Financial Auditing Process Through Blockchain Technology, using Identity Based Cryptography

With the realizing need to control fraud in the private and the public sector, we observe the past trends of corporate frauds, in terms of money laundering, counterfeit, misrepresentation of financial records by deviating rules, this mostly attracts developing economies which has spread its attention in various branches of the nation thus providing the right opportunity for criminal minds to set at work. Our paper focuses to, an aspect of accounting and helping auditors to prevent corporate fraud at an early stage. Paper also critically analyzes the impacts and scope of blockchain technology for auditing with the strong literature review in the field of blockchain technology and its application in the audit process. We have proposed a framework which uses the blockchain based IBC (Identity based cryptography), for blockchain node creation which will be added to the blockchain network. Any financial transaction can be properly audit with the implementation of this framework, as blockchain technology provides a very secure platform for the transaction and at the very same time auditing can also be completed because entire system of blockchain is transparent.
An improved target detection method based on multiscale features fusion

Abstract A pedestrian‐target detection method based on multiscale convolution features is proposed to solve the problem of the existing target detection algorithms based on convolutional neural networks that cannot effectively adapt to the target scale change, its deformation, and the complex environment. This article analyzes the characteristics of small target, establishes the a three‐layer pyramid network structure based on horizontal connection fusion, and makes full use of shallow convolution features with lower semantics to improve the accuracy of small target detection; From the angle of the cross ratio, quantitative analysis is made on the effect of eliminating redundant bounding box by nonmaximum suppression based on center point when the window with the cross ratio is less than 0.7, solves the problem of missing detection of overlap and small target, and improves the target detection accuracy and the generalization ability of the target detection model. Through the experimental results on VOC2007 and VOC2012 public data sets, the proposed method of this article has higher detection accuracy and stability for small and incomplete targets, multiscale target detection, and target classification under the complex environment.
Drone Navigation and Avoidance of Obstacles Through Deep Reinforcement Learning

Unmanned aerial vehicles (UAV) specifically drones have been used for surveillance, shipping and delivery, wildlife monitoring, disaster management etc. The increase on the number of drones in the airspace worldwide will lead necessarily to full autonomous drones. Given the expected huge number of drones, if they were operated by human pilots, the possibility to collide with each other could be too high. In this paper, deep reinforcement learning (DRL) architecture is proposed to make drones behave autonomously inside a suburb neighborhood environment. The environment in the simulator has plenty of obstacles such as trees, cables, parked cars and houses. In addition, there are also another drones, acting as moving obstacles, inside the environment while the learner drone has a goal to achieve. In this way the drone can be trained to detect stationary and moving obstacles inside the neighborhood and so the drones can be used safely in a public area in the future. The drone has a front camera and it can capture continuously depth images. Every depth image is part of the state used in DRL architecture. Also, another part of the state is the distance to the geo-fence (a virtual barrier on the environment) which is added as a scalar value. The agent will be rewarded negatively when it tries to overpass the geo-fence limits. In addition, angle to goal and elevation angle between the goal and the drone will be used as information to be added to the state. It is considered that these scalar values will improve the DRL performance and also the reward obtained. The drone is trained using Q-Network and its convergence and final reward are evaluated. The states containing image and several scalars are processed by a neural network that joints the two state parts into a unique flow. This neural network is named as Joint Neural Network (JNN) [1]. The training and test results show that the agent can successfully learn to avoid any obstacle in the environment. The results for three scenarios are very promising and the learner drone reaches the destination with a success rate 100% in first two tests and with a success rate 98% in the last test, this one with a total of three drones.
Visual Interpretation of Recurrent Neural Network on Multi-dimensional Time-series Forecast

Recent attempts at utilizing visual analytics to interpret Recurrent Neural Networks (RNNs) mainly focus on natural language processing (NLP) tasks that take symbolic sequences as input.However, many real-world problems like environment pollution forecasting apply RNNs on sequences of multi-dimensional data where each dimension represents an individual feature with semantic meaning such as PM 2.5 and SO 2 .RNN interpretation on multi-dimensional sequences is challenging as users need to analyze what features are important at different time steps to better understand model behavior and gain trust in prediction.This requires effective and scalable visualization methods to reveal the complex many-to-many relations between hidden units and features.In this work, we propose a visual analytics system to interpret RNNs on multi-dimensional time-series forecasts.Specifically, to provide an overview to reveal the model mechanism, we propose a technique to estimate the hidden unit response by measuring how different feature selections affect the hidden unit output distribution.We then cluster the hidden units and features based on the response embedding vectors.Finally, we propose a visual analytics system which allows users to visually explore the model behavior from the global and individual levels.We demonstrate the effectiveness of our approach with case studies using air pollutant forecast applications.
Towards Imbalanced Image Classification: A Generative Adversarial Network Ensemble Learning Method

Learning from minority class has been a significant and challenging task which has many potential applications. Weather classification is such a case of imbalanced label distribution. This is because in places like Beijing, some types of weather, such as rain and snow, are relatively rare compared to sunny and haze days. Existing methods are primary to classify the weather conditions relying on expensive sensors or human assistance, which however usually are expensive and time-consuming. In this paper, we propose a new ensemble framework based on the advanced generative adversarial network and an effective data cleaning way to address the class imbalance problem for weather classification. The proposed method not only generates new and reliable samples for the minority class to restore balance, but also filters those generated samples which are unreliable. Experiments show that our approach outperforms the state-of-the-art methods by a huge margin for imbalanced weather classification on several benchmark data sets.
Coverage Guided Differential Adversarial Testing of Deep Learning Systems

Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The incorrect behaviors obtained by DLFuzz are then exploited for retraining and improving the dependability of the models. We present empirical evaluations on two well-known data-sets to demonstrate its effectiveness. Compared with DeepXplore, the state-of-the-art deep learning white-box testing framework, DLFuzz does not require extra efforts to find similar functional deep learning systems for cross-referencing check. But DLFuzz could generate $338.59\%$ more adversarial inputs with $89.82\%$ smaller perturbations, while maintaining the identities of the original inputs. DLFuzz also managed to averagely obtain $2.86\%$ higher neuron coverage, and save $20.11\%$ time consumption with respect to DeepXplore. We then evaluate the effectiveness of strategies for neuron selection, and demonstrated that all these strategies perform better than DeepXplore. Finally, DLFuzz proved to be able to improve the accuracy of deep learning systems by incorporating these adversarial inputs to retrain.