Reviewerly/exHarmony huggingface dataset:
Echo State Network and Echo State Gaussian Process for Non-Line-of-Sight Target Tracking

Non-line-of-sight (NLOS) propagation blocks the direct path between the anchors and the agent, therefore, results in large positive bias in localization. Mobile target tracking in a NLOS scenario is much more challenging than static localization because of the need of not only dynamic modeling but also NLOS error mitigation. Gaussian Process (GP) regression is the state-of-the-art machine learning approach that resolves the issue. This article proposes two novel nonparametric strategies for NLOS target tracking, namely Echo State Network (ESN) and Echo State Gaussian Process (ESGP), both of which offers significant enhancement to the tracking performance compared to GP regression. Moreover, ESN is much more computationally efficient than GP regression and ESGP also provides a measure of confidence on the tracking results. Monte Carlo simulations and experiments prove the robustness and effectiveness of ESN and ESGP.
Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider

The cloud computing environment provides easy-to-access service for private and confidential data. However, there are many threats to the leakage of private data. This paper focuses on investigating the vulnerabilities of cloud service providers (CSPs) from three risk aspects: management risks, law risks, and technology risks. Additionally, this paper presents a risk assessment model that is based on grey system theory (GST), defines indicators for assessment, and fully utilizes the analytic hierarchy process (AHP). Furthermore, we use the GST to predict the risk values by using the MATLAB platform. The GST determines the bottom evaluation sequence, while the AHP calculates the index weights. Based on the GST and the AHP, layer-based assessment values are determined for the bottom evaluation sequence and the index weights. The combination of AHP and GST aims to obtain systematic and structured well-defined procedures that are based on step-by-step processes. The AHP and GST methods are applied successfully to handle any risk assessment problem of the CSP. Furthermore, substantial challenges are encountered in determining the CSP's response time and identifying the most suitable solution out of a specified series of solutions. This issue has been handled using two additive features: the response time and the grey incidence. The final risk values are calculated and can be used for prediction by utilizing the enhanced grey model (EGM) (1,1), which reduces the prediction error by providing direct forecast to avoid the iterative prediction shortcoming of standard GM (1,1). Thus, EGM (1,1) helps maintain the reliability on a larger scale despite utilizing more prediction periods. Based on the experimental results, we evaluate the validity, accuracy, and response time of the proposed approach. The simulation experiments were conducted to validate the suitability of the proposed model. The simulation results demonstrate that our risk assessment model contributes to reducing deviation to support CSPs with the three adopted models.
Quantum Attacks and Defenses for Proof-of-Stake

Advances in both quantum computation and blockchain systems necessitate a rigid analysis for the security of blockchains in the advent of powerful quantum computers. Computational problems that are proved or believed to be intractable by classical computers are known to be attacked by well-studied quantum algorithms. Such problems are used as basis to secure blockchains and hence the security of such systems must be defended. As proof-of-work is used in early blockchains to maintain consensus, proof-of-stake was proposed to lower its high energy consumption and provide a higher network throughput. Proof-of-Stake has hence got an increasing interest, variations, implementations and enhancements whilst its quantum security defenses remained almost the same. By comparing proof-of-work to proof-of-stake, we find that the latter is more vulnerable to certain attack vectors. We suggest general defenses for quantum-resilient blockchains and thoroughly analyze post-quantum signature schemes to select an appropriate alternative to the most serious threat on conventional digital signatures.
Calibration on Camera’s Intrinsic Parameters Based on Orthogonal Learning Neural Network and Vanishing Points

According to the orthogonal characteristics of the rotation matrix, a novel technique on camera's intrinsic parameters calibration is proposed in this paper. Firstly, two sets parallel lines in the space are designed which are perpendicular, and a corresponding equation set is obtained according to their coordinates in space and in image plane. Then an orthogonal learning neural network is designed, trained, and 8 elements of camera project matrix are obtained according to the eigenvector corresponding to the minimal eigen-value. Similarly, according to the solution requirement, another orthogonal learning neural network is designed, so the camera's principal coordinates (u0, v0) and the amplification factors kx and ky are obtained respectively. Finally the simulation results show that the proposed method has good robustness, and orthogonality test shows that the proposed method is of high precision.
Comprehensive Overview of Selfish Mining and Double Spending Attack Countermeasures

Bitcoin is a well-known cryptocurrency in which records of transactions are maintained by a P2P network to create a distributed ledger. Due to the complex nature of maintaining highly efficient, transparent and speed transactions between nodes, security is one of the primary concerns of this system. The most prominent modern-day attacks to this system are the selfish mining and double spending attacks. This survey is organized around the aspects pertaining to countermeasures of selfish mining and double spending attacks. We selected a total of 20 primary studies from recent years as a result of a systematic analysis approach. In this study we will aim to classify, analyze, and evaluate these proposed methods in order to create a secure blockchain system. These countermeasures are being developed and are being experimented with in order to mitigate and/or even eliminate these attacks from occurring. These proposed frameworks and research papers will outline the keys to improving the blockchain architecture with code scripts and design parameters to increase the functionality of the blockchain. We aim to identify implications of these countermeasures to address vulnerabilities in the blockchain network for future research on this topic.
Degrees of freedom and model selection for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e11610" altimg="si5.svg"><mml:mi>k</mml:mi></mml:math>-means clustering

A thorough investigation into the model degrees of freedom in k-means clustering is conducted. An extension of Stein's lemma is used to obtain an expression for the effective degrees of freedom in the k-means model. Approximating the degrees of freedom in practice requires simplifications of this expression, however empirical studies evince the appropriateness of the proposed approach. The practical relevance of this new degrees of freedom formulation for k-means is demonstrated through model selection using the Bayesian Information Criterion. The reliability of this method is then validated through experiments on simulated data as well as on a large collection of publicly available benchmark data sets from diverse application areas. Comparisons with popular existing techniques indicate that this approach is extremely competitive for selecting high quality clustering solutions.
Exploration of Vulnerabilities, Threats and Forensic Issues and its impact on the Distributed Environment of Cloud and its mitigation

Cloud computing environment offers many services which have attracted the criminals or oppugners to commit cyber-crimes in a more sophisticated manner. Most of the crimes conducted by the criminals are data tampering, spam assaults, distributed denial of service attack and many others. There is a deficiency of reports which relates crime and the usage of cloud computing services. This paper discusses about the threat model STRIDE and the ill-use of the cloud services for many mischievous purpose by the oppugners around the world. This paper employs STRIDE threat modelling approach as a reference to relate the attacks to threats. Some of the malicious activities committed using cloud are distribution of considerable amount of spams, using the cloud service provider's reputation to cheat the firewalls, distributed denial of service, virus dissemination, credit card fraud and arraying the botnet command and control servers. This paper also discusses on a few real websites which have been affected by threats in the recent years. Most of the criminals get away because of lack of evidence. Evidence collection and analysis in the digital world and especially in cloud environment is a great challenge. This paper also touches on a few issues of forensics in cloud computing environment.
Sentiment Analysis of Arabic Algerian Dialect Using a Supervised Method

Sentiment analysis holds an important place in Natural Language Processing (NLP) due to its utility in resolving different issues in many fields such as e-commerce, politic sciences, social media analysis, cybersecurity, etc. In fact, most of the work done in the field has been dedicated to the sentiment of English texts. However, adapting research findings to the Arabic language and its dialects is not trivial because of their linguistic features. In this work, we apply two supervised methods, namely, Deep Learning and Support Vector Machines (SVM), for sentiment analysis of Modern Arabic and the Algerian dialect. These methods have been applied on a huge annotated dataset collected from different Arabic Algerian sources. Findings are showing promising results in sentiment analysis of the Arabic Algerian dialect.
Lexical dispersion and corpus design

Abstract Lexical dispersion is typically measured across arbitrary corpus parts of equal size. In this study, we apply D A – a new dispersion index designed for unequal-sized corpus parts – to the British National Corpus (BNC) in a series of cases studies to show that the dispersion of a word is strongly influenced by the corpus units or parts it is measured across. Our results show that dispersion should be measured and interpreted based on corpus units that are linguistically meaningful for a particular research goal. We conclude with recommendations to help researchers select meaningful corpus units for measuring and interpreting lexical dispersion.
Probabilistic prediction of solar power supply to distribution networks, using forecasts of global horizontal irradiation

This paper presents a mathematical model for the prediction of the probabilities of reverse power flow exceeding predefined critical thresholds at feed-in points of a distribution network. The parametric prediction model is based on hourly forecasts of global horizontal irradiation and uses copulas, a tool for modeling the joint probability distribution of two or more strongly correlated random variables with non-Gaussian (marginal) distributions. The model is used for determining the joint distribution of forecasts of global horizontal irradiation and measured solar power supply at given feed-in points, where respective sample datasets were provided by Deutscher Wetterdienst and the N-ERGIE Netz GmbH. It is shown that the fitted model replicates important characteristics of the data such as the corresponding marginal densities. The validation results highlight strong performance of the proposed model. The copula-based model enables to predict the distribution of solar power supply conditioned on the forecasts of global horizontal irradiation, thus anticipating great fluctuations in the distribution network.
A self-powered analog sensor-data-logging device based on Fowler-Nordheim dynamical systems

Abstract Continuous, battery-free operation of sensor nodes requires ultra-low-power sensing and data-logging techniques. Here we report that by directly coupling a sensor/transducer signal into globally asymptotically stable monotonic dynamical systems based on Fowler-Nordheim quantum tunneling, one can achieve self-powered sensing at an energy budget that is currently unachievable using conventional energy harvesting methods. The proposed device uses a differential architecture to compensate for environmental variations and the device can retain sensed information for durations ranging from hours to days. With a theoretical operating energy budget less than 10 attojoules, we demonstrate that when integrated with a miniature piezoelectric transducer the proposed sensor-data-logger can measure cumulative “action” due to ambient mechanical acceleration without any additional external power.
MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation

Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated in social network services. However, most existing research either predicts users’ consumption preferences or recommends friends to users without dealing with them simultaneously. We propose a holistic approach to predict users’ preferences on friends and items jointly and thereby make better recommendations. To this end, we design a graph neural network that incorporates a mutualistic mechanism to model the mutual reinforcement relationship between users’ consumption behaviors and social behaviors. Our experiments on the two-real world datasets demonstrate the effectiveness of our approach in both social recommendation and link prediction.
Evaluation of Dimensionality Reduction Techniques for Big data

In this digital era, big data has very high dimension and requires large amount of space for its data storage. Hence a lossless data interpretation will be difficult when big data contains large dimension. But, all these dimensions in big data may not be relevant or they may be interrelated and hence redundancy may exist in attribute set. Dimensionality reduction is a technique which focusses on downsizing the attributes and complication of a high dimensional data. In this paper, a detailed study of different dimensionality reduction techniques namely principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), singular value decomposition (SVD), independent component analysis (ICA) has been proposed. Furthermore, it also provides comparative analysis based on various parameters.
Contrast Improvement using Local Gamma Correction

This paper represents a local Gamma-correction technique that tries to supplement the limitations of the conventional Gamma-correction scheme. Gamma correction is a widely used contrast manipulation scheme which is broadly employed in many image enhancement applications. However, this technique is able to enhance a particular range of intensity levels and simultaneously fails to enhance the other intensity regions in an image. An image with different grades of intensity regions can't be enhanced simultaneously using a single gamma value. To overcome this problem, an adaptive local gamma correction technique is proposed here. In this method, the gamma factor is varied locally according to the local mean intensity of a neighborhood. As a result, the local gamma values corresponding to the different grades of intensity range would be different resulting in effective contrast manipulation of an image. This results in contrast improvement of different image regions of dissimilar grade of intensity range. Simulation results reveal that the proposed technique subjectively provides superior performance than many of the existing image enhancement schemes.
Fast SIFT Feature Matching Algorithm Based on Geometric Transformation

Structure from Motion (SfM) is a series of methods for reconstructing scene structure (i.e.three-dimensional space points) and camera motion (i.e.camera pose) from an image set.In this paper, aimed at the low accuracy of the global reconstruction, the robustness to external points and the timeconsuming incremental reconstruction, a fast, closed-loop and high precision reconstruction method is proposed.The method is based on the SIFT matching algorithm GeoMatch, which is constrained by geometric structure of the scene and by numerical and statistical characteristics of feature invariant scale transformation.Experiments show that GeoMatch outperforms both traditional tree-based and hash-based matching methods in terms of time and accuracy.
WordChange: Adversarial Examples Generation Approach for Chinese Text Classification

As an important carrier for disseminating information in the Internet Age, the text contains a large amount of information.In recent years, adversarial example attacks against text discrete domains have been received widespread attention.Deep neural network (DNN) produces opposite predictions by adding small perturbations to the text data.In this paper, we present ''WordChange'': an adversarial examples generation approach for Chinese text classification based on multiple modification strategies, and we evaluate the effectiveness of the method in sentiment analysis dataset and spam dataset.This method effectively locates important word positions by designing a keyword contribution algorithm.We first propose a ''word-split'' strategy to substitute keywords thatare designed by the structure and semantic property of Chinese texts.We also first apply ''swap'' and ''insert'' strategies on Chinese texts to generate adversarial examples.We further discuss the influence of multiple Chinese Word Segmentation tools and different text lengths on the proposed method, as well as the diversification of Chinese text modification strategies.Finally, the adversarial texts based on the long short-term memory network (LSTM) can be successfully transferred to other text classifiers and real-world applications.
Doctor’s Cursive Handwriting Recognition System Using Deep Learning

Handwriting is a skill to express thoughts, ideas, and language. Over the years, medical doctors have been well-known for having illegible cursive handwritings and has been a generally accepted matter. The datasets used in this paper are samples of doctors cursive handwriting collected from several clinics and hospitals of Metro Manila, Quezon City and Taytay, Rizal. In this paper, we present the Handwriting Recognition System using Deep Convolutional Recurrent Neural Network that is developed in order to identify the text in the image of prescriptions written by the doctors and show the readable text conversion of the cursive handwriting. In this study two models were evaluated and based on the experimentation CRNN with model-based normalization scheme than the CRNN alone. This study achieved 76% training accuracy rate and the developed model was found successfully implemented in a mobile application, having achieved a validation accuracy of 72% for the validation set from the remaining 540 images of prescription. The mobile application was validated for the second time using the captured 48 handwriting samples written by the researchers and correctly identified 17 images out of 48 this gives us a 35% validation accuracy.
Learning to find reliable correspondences with local neighborhood consensus

Seeking reliable correspondences has been always the key of achieving image matching task in computer vision. The recent work LFGC aims to achieve this goal by training a binary correspondence classifier, which infers the probabilities of correspondences being inliers and regresses the relative camera pose via learning the input correspondence data. Specially, the LFGC employs multi-layer perceptrons to extract features and context normalization to model underlying contextual relation of correspondences. Nevertheless, the contextual relation obtained by context normalization is easily interfered by outliers, which limits the accuracy and robustness of the correspondence classifier. To address this limitation, based on the observation that the contextual relation in spatial neighborhood is generally well preserved on most scenarios, this paper proposes an improved strategy, i.e., maximizing local neighborhood consensus to establish robust neighborhood context. Specifically, we construct the neighborhood for correspondences and preserve neighbors’ features similar to their medoids, where the neighborhood information is embedded. Subsequently, we combine this neighborhood structure with multi-layer perceptrons to classify the correspondences as inliers or outliers, simultaneously potential inliers are employed to regress the relative camera pose encoded by the essential matrix. Qualitative and quantitative experimental results on benchmark datasets demonstrate that the improved network can effectively promote performance in outlier removal and camera pose estimation comparing with the baseline.
Crime Analysis and Prediction using Optimized K-Means Algorithm

In India, the crime rate is increasing each day. In the current situation, recent technological influence, effects of social media and modern approaches help the offenders to achieve their crimes. Both analysis and prediction of crime is a systematized method that classifies and examines the crime patterns. There exist various clustering algorithms for crime analysis and pattern prediction but they do not reveal all the requirements. Among these, K means algorithm provides a better way for predicting the results. The proposed research work mainly focused on predicting the region with higher crime rates and age groups with more or less criminal tendencies. We propose an optimized K means algorithm to lower the time complexity and improve efficiency in the result.
Analyzing Sentiment in Tamil Tweets using Deep Neural Network

Sentiment Analysis (SA) is the process of extracting the information from the given text in which the text consists of various emotions such as happy, sad, proud, fear and so on about various events, people, system, and facts. Since the social media is burgeoned with huge amount of data, it is necessary to derive some useful information from the available data. This extracted useful information can be used in product purchase, politics, sports, and more. To be precise, it helps in decision making. For implementing the sentiment analysis, a lot of research work has been carried out which ranges from lexicon based approach to machine learning based approach. In recent days deep learning and neural networks plays a major role in sentiment analysis and it is considered as a state-of-the art method for analysing various languages. Tamil is one of the Indian languages which still requires state-of-art model for sentiment analysis. The language specific features, grammar structure, agglutinative nature of Tamil language poses more challenges. In this paper, we proposed a combined character based Deep Bidirectional long short-term memory neural networks (DBLS TM) to analyse the Tamil tweets.