Reviewerly/exHarmony huggingface dataset:
Traceable Quantum Steganography Scheme Based on Pixel Value Differencing

Abstract A novel and traceable quantum steganography scheme based on pixel value differencing (PVD) is proposed. In the proposed scheme, a quantum cover image is divided into non-overlapping blocks of two consecutive pixels. Then, by a series of reversible logic circuits, we calculate the difference value based on the values of the two pixels in each block and classify it as one of a set of continuous ranges. The secret image and operator information are embedded in the cover image by using the new obtained difference value to replace the original one. The number of bits of secret image that can be embedded in a block is determined, and the number of bits of operator information is decided by the range of the difference value belongs to. Moreover, when the embedded data is extracted from a stego image, it is not necessary to refer to the original cover image. The performance of the proposed scheme is based on the analysis of several categories of simulation results, such as visual quality, capacity, and robustness.
Augmented reality applications: A Meta-analysis and thematic analysis

﻿The purpose of this study is to determine the effect of augmented reality (AR) applications on academic success through meta-analytic and thematic analysis. 51 items from 45 studies conducted between 2013 and 2019 were examined in different databases on the effect size of AR in relation on academic success, education level, and duration of the application. The effect size was found to exist at a medium level, considered to be significant which means that AR has positive effect on academic success. In terms of thematic analysis, with reference to common codes and themes found in qualitative studies conducted both nationally and internationally, qualitative findings were obtained. An emphasis on the effect of AR on learning, class environment and affective dimensions was made, together with a consideration of some limitations. It was stated that learning environments could be arranged in such a way that students could be motivated through the use of AR, leading to permanent and solid learning. Therefore, careful and planned use of AR can be said to encourage success.
Evaluation of E-learning Websites Using the Webqual Method and Importance Performance Analysis

Participant-centered training that uses digital classes leads to changes in the current learning system that focuses on the domain of knowledge. The use of technology in the modern era can be a solution to overcome the issue of expenditure and the quality of educational institutions. The Importance Performance Analysis (IPA) and Webqual methods were chosen in this study because Webqual can translate user opinions in the form of questionnaires and the IPA method is used in managing the results of questionnaire data so that it can produce IPA quadrants. The purpose of this study is to evaluate e-learning websites based on the results of the webqual questionnaire and calculations with the importance-performance analysis method. The questionnaire was distributed to 95 students with various types of study programs at Stikubank University. The results of the calculation of the IPA method indicate that the average student is satisfied with the quality of the e-learning website at 86.75%. Although there are still some parts that can be improved on e-learning websites such as clear instructions, accurate information, and ease of communication.
Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma

Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). We extend the method to bidirectional LSTMs (BiLSTMs) and use it in the context of predicting future clinical outcomes using patients' EHR historical visits.We use a real EHR dataset comprising 11071 patients, to evaluate and compare CD interpretations from LSTM and BiLSTM models. First, we train LSTM and BiLSTM models for the task of predicting which pre-school children with respiratory system-related complications will have asthma at school-age. After that, we conduct quantitative and qualitative analysis to evaluate the CD interpretations produced by the contextual decomposition of the trained models. In addition, we develop an interactive visualization to demonstrate the utility of CD scores in explaining predicted outcomes.Our experimental evaluation demonstrate that whenever a clear visit-level pattern exists, the models learn that pattern and the contextual decomposition can appropriately attribute the prediction to the correct pattern. In addition, the results confirm that the CD scores agree to a large extent with the importance scores generated using logistic regression coefficients. Our main insight was that rather than interpreting the attribution of individual visits to the predicted outcome, we could instead attribute a model's prediction to a group of visits.We presented a quantitative and qualitative evidence that CD interpretations can explain patient-specific predictions using CD attributions of individual visits or a group of visits.
Enabling real-time road anomaly detection via mobile edge computing

To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node. (2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.
Advancing Automation in Digital Forensic Investigations Using Machine Learning Forensics

In the last few years, most of the data such as books, videos, pictures, medical and even the genetic information of humans are moving toward digital formats.Laptops, tablets, smartphones and wearable devices are the major source of this digital data transformation and are becoming the core part of our daily life.As a result of this transformation, we are becoming the soft target of various types of cybercrimes.Digital forensic investigation provides the way to recover lost or purposefully deleted or hidden files from a suspect's device.However, current man power and government resources are not enough to investigate the cybercrimes.Unfortunately, existing digital investigation procedures and practices require huge interaction with humans; as a result it slows down the process with the pace digital crimes are committed.Machine learning (ML) is the branch of science that has governs from the field of AI.This advance technology uses the explicit programming to depict the human-like behaviour.Machine learning combined with automation in digital investigation process at different stages of investigation has significant potential to aid digital investigators.This chapter aims at providing the research in machine learning-based digital forensic investigation, identifies the gaps, addresses the challenges and open issues in this field.
The weak call-by-value λ-calculus is reasonable for both time and space

We study the weak call-by-value λ-calculus as a model for computational complexity theory and establish the natural measures for time and space — the number of beta-reduction steps and the size of the largest term in a computation — as reasonable measures with respect to the invariance thesis of Slot and van Emde Boas from 1984. More precisely, we show that, using those measures, Turing machines and the weak call-by-value λ-calculus can simulate each other within a polynomial overhead in time and a constant factor overhead in space for all computations terminating in (encodings of) ”true” or ”false”. The simulation yields that standard complexity classes like P , NP , PSPACE , or EXP can be defined solely in terms of the λ-calculus, but does not cover sublinear time or space. Note that our measures still have the well-known size explosion property, where the space measure of a computation can be exponentially bigger than its time measure. However, our result implies that this exponential gap disappears once complexity classes are considered instead of concrete computations. We consider this result a first step towards a solution for the long-standing open problem of whether the natural measures for time and space of the λ-calculus are reasonable. Our proof for the weak call-by-value λ-calculus is the first proof of reasonability (including both time and space) for a functional language based on natural measures and enables the formal verification of complexity-theoretic proofs concerning complexity classes, both on paper and in proof assistants. The proof idea relies on a hybrid of two simulation strategies of reductions in the weak call-by-value λ-calculus by Turing machines, both of which are insufficient if taken alone. The first strategy is the most naive one in the sense that a reduction sequence is simulated precisely as given by the reduction rules; in particular, all substitutions are executed immediately. This simulation runs within a constant overhead in space, but the overhead in time might be exponential. The second strategy is heap-based and relies on structure sharing, similar to existing compilers of eager functional languages. This strategy only has a polynomial overhead in time, but the space consumption might require an additional factor of log n , which is essentially due to the size of the pointers required for this strategy. Our main contribution is the construction and verification of a space-aware interleaving of the two strategies, which is shown to yield both a constant overhead in space and a polynomial overhead in time.
Assessment of Major and Trace Elements in Drinking Groundwater in Bisha Area, Saudi Arabia

Drinking groundwater represents 30% of the world’s fresh water and 0.9% of the whole world’s water. Therefore, routine analysis and monitoring of the groundwater is a paramount issue, specifically the measurement of elemental concentrations due to aquifer characterization. Consequently, the purpose of this study was to determine major and trace elements in groundwater. In total, 25 samples of groundwater were collected from wells in the Bisha area, Asir province, Saudi Arabia. All samples were analyzed for major and trace elements by using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). In total, 15 elements were measured including four major elements (Na, K, Mg, and Ca) and 11 trace elements (V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Cd, and Pb). Major elements (Na, Mg, and Ca) exceeded the guideline limits in some samples. In addition, only one trace element (Se) exceeded the World Health Organization (WHO) permissible limits in some samples. This could be due to rock characteristics in aquifers. Very hard water was shown in 92% of the samples. Moreover, a high percentage (32%) of the analyzed samples also exceeded the guideline levels for chloride. ANOVA analysis showed significant difference (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn>0.05</mml:mn></mml:mrow></mml:math>) between Bisha samples (North and South), Bisha samples (North), and the remaining samples, for V and pH, and Na, Cl − , EC, and TDS, respectively. No significant differences (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>0.05</mml:mn></mml:mrow></mml:math>) were reported for Na, K, Mg, Ca, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Cd, and Pb between all samples. In general, 25 significant (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>0.05</mml:mn></mml:mrow></mml:math>) correlations were reported among the measured elements. For the positive correlations, similar distribution for the elements is anticipated. In conclusion, the groundwater in this study is not suitable for domestic use due to its hardness and only some are suitable for irrigation. More studies are needed to confirm our findings in the study area.
Dynamics of Rydberg excitations and quantum correlations in an atomic array coupled to a photonic crystal waveguide

We study the dynamics of up to two Rydberg excitations and the correlation growth in a chain of atoms coupled to a photonic crystal waveguide. In this setup, an excitation can hop from one atom to another via exponentially decaying exchange interactions mediated by the waveguide. An initially localized excitation undergoes a continuous-time quantum walk for short-range hopping, and for long-range, it experiences quasi-localization. Besides that, the inverse participation ratio reveals a super-ballistic diffusion of the excitation in short times, whereas, at a long time, it becomes ballistic. For two initially localized excitations, intriguing, and complex dynamical scenarios emerge for different initial separations due to the competition between the Rydberg-Rydberg and exchange interactions. In particular, the two-point correlation reveals a light-cone behavior even for sufficiently long-range exchange interactions. Additionally, we characterize the growth of bipartite entanglement entropy, which exhibits a global bound if only one excitation is present in the dynamics. Finally, we analyze the effect of imperfections due to spontaneous emission from the Rydberg state into photons outside the waveguide and show that all physical phenomena we predict are well within experimental reach.
Growth of genuine multipartite entanglement in random unitary circuits

We study the growth of genuine multipartite entanglement in random quantum circuit models, which include random unitary circuit models and the random Clifford circuit. We find that for the random Clifford circuit, the growth of multipartite entanglement remains slower in comparison to the random unitary case. However, the final saturation value of multipartite entanglement is almost the same in both cases. The behavior is then compared to the genuine multipartite entanglement obtained in random matrix product states with a moderately high bond dimension. We then relate the behavior of multipartite entanglement to other global properties of the system, viz. the delocalization of the many-body wavefunctions in Hilbert space. Along with this, we analyze the robustness of such highly entangled quantum states obtained through random unitary dynamics under weak measurements.
Article Classification using Natural Language Processing and Machine Learning

Text classification is an important task which may help human reducing time and effort. This work is aimed to propose an approach for text classification, especially for articles. The proposed method can automatically extract information and categorize articles on suitable topics. The input data were pre-processed, extracted, vectorized and classified using machine learning techniques including Support Vector Machines, Naïve Bayes, and k-Nearest Neighbors. The experiments were carried out on two data sets of articles showed that with the accuracy of over 91%, using natural language processing and support vector machines technique proved its feasibility for developing the automatic classification system of articles.
An Efficient Total Variation Minimization Method for Image Restoration

In this paper, we present an effective algorithm for solving the Poisson-Gaussian total variation model.The existence and uniqueness of solution for the mixed Poisson-Gaussian model are proved.Due to the strict convexity of the model, the split-Bregman method is employed to solve the minimization problem.Experimental results show the effectiveness of the proposed method for mixed Poisson-Gaussion noise removal.Comparison with other existing and well-known methods is provided as well.
SGCN: A Graph Sparsifier Based on Graph Convolutional Networks

Graphs are ubiquitous across the globe and within science and engineering. With graphs growing in size, node classification on large graphs can be space and time consuming, even with powerful classifiers such as Graph Convolutional Networks (GCNs). Hence, some questions are raised, particularly, whether one can keep only some of the edges of a graph while maintaining prediction performance for node classification, or train classifiers on specific subgraphs instead of a whole graph with limited performance loss in node classification. To address these questions, we propose Sparsified Graph Convolutional Network (SGCN), a neural network graph sparsifier that sparsifies a graph by pruning some edges. We formulate sparsification as an optimization problem, which we solve by an Alternating Direction Method of Multipliers (ADMM)-based solution. We show that sparsified graphs provided by SGCN can be used as inputs to GCN, leading to better or comparable node classification performance with that of original graphs in GCN, DeepWalk, and GraphSAGE.
Intrinsic decoherence effects on measurement-induced nonlocality

By considering an exactly solvable model of a two interacting spin-1/2 qubits described by the Heisenberg anisotropic interaction in the presence of intrinsic decoherence, we study the dynamics of entanglement quantified by the concurrence and measurement-induced nonlocality (MIN) based on Hilbert-Schmidt norm and trace distance with different initial conditions. We highlight the relationship between the entanglement and MIN for the pure initial state. For an initial separable state, it is found that the robustness and the generation of the quantum correlations depend on the physical parameters. While considering the entangled state as an initial state, the results show that despite the phase decoherence all the correlations reach their steady state values after exhibiting some oscillations. Finally, we show that the existence of quantum correlation captured by MIN in the unentangled state.
Comprehensively Meld Code Clone Identifier for Replicated Source Code Identification in Diverse Web Browsers

The emergence and the progress in the process of reusing the software’s, has caused difficulties in the maintaining the software codes and the corresponding depositories. Cloning of software codes is the important reason behind the arising difficulties in the maintenance of the software codes and the depositories. The cloning of codes is a process of replicating the existing codes for utilizing it elsewhere within the software system. The copying and pasting of the fragments of code is also well thought-out as the method of code cloning causing difficulties in the software maintenance. The maintenance of software is described as the alteration performed over the existing software on the completion of the development as well as the implementation process. Utilizing the maintenance process in the software the software companies deliver the improvements and the additional enhancements according to the working circumstance. The maintenance mainly focuses on removing the bugs and to repair the identified faults in the time of execution to enhance the performance. The work focused on the paper is mainly a comprehensive study over the prevailing tools laid out in the process of code-clone identification. The different techniques employed, the challenges incurred, the blunders made in the development, the enhanced refactoring challenges and efforts in the comprehension of codes are explored in the paper. From the study it was understood that the code clone identification using the web based tools are more advantageous than engaging algorithms in identifying the clones. So the a hybridized (meld) web based code clone identifier tool is engaged in the process of identifying the cloning of codes in diverse web browsers in exhibited in the paper. The exhibited tool equips a highly powerful tool for detecting the clones in a precise an efficient manner. The duplication of codes are often vulnerable and could be malicious. So the exhibited work in the future concentrates in developing an extended tool to identify the malicious codes to improvise the process of code clone identification making it concise and effective.
Fast Threshold ECDSA with Honest Majority

ECDSA is a widely adopted digital signature standard. A number of threshold protocols for ECDSA have been developed that let a set of parties jointly generate the secret signing key and compute signatures, without ever revealing the signing key. Threshold protocols for ECDSA have seen recent interest, in particular due to the need for additional security in cryptocurrency wallets where leakage of the signing key is equivalent to an immediate loss of money. We propose a threshold ECDSA protocol secure against an active adversary in the honest majority model with abort. Our protocol is efficient in terms of both computation and bandwidth usage, and it allows the parties to pre-process parts of the signature, such that once the message to sign becomes known, the they can compute a secret sharing of the signature very efficiently, using only local operations. We also show how to obtain fairness in the online phase at the cost of some additional work in the pre-processing, i.e., such that it either aborts during pre-processing phase, in which case nothing is revealed, or the signature is guaranteed to be delivered to all honest parties.
LGM Split Sampler: An Efficient MCMC Sampling Scheme for Latent Gaussian Models

A general and flexible class of latent Gaussian models is proposed in this paper.The latent Gaussian model is adapted to the generalized additive model for location, scale and shape (GAMLSS), that is, the data density function of each data point can depend on more than a single linear predictor of the latent parameters.We refer to this framework as extended latent Gaussian models.The most commonly applied latent Gaussian models (LGMs) are such that a linear predictor is proposed only for the location parameter.Extended LGMs allow proposing linear predictors also for the scale parameter and potentially other parameters.We propose a novel computationally efficient Markov chain Monte Carlo sampling scheme for the extended LGMs which we refer to as the LGM split sampler.It is a two block Gibbs sampling scheme designed to exploit the model structure of the extended LGMs.An extended LGM is constructed for a simulated dataset and the LGM split sampler is implemented for posterior simulations.The results demonstrate the flexibility of the extended LGM framework and the efficiency of the LGM split sampler.