Reviewerly/exHarmony huggingface dataset:
Bi-level multi-objective evolution of a Multi-Layered Echo-State Network Autoencoder for data representations

The Multi-Layered Echo-State Network (ML-ESN) is a recently developed, highly powerful type of recurrent neural network. It has succeeded in dealing with several non-linear benchmark problems. On account of its rich dynamics, ML-ESN is exploited in this paper, for the first time, as a recurrent Autoencoder (ML-ESNAE) to extract new features from original data representations. Further, the challenging and crucial task of optimally determining the ML-ESNAE architecture and training parameters is addressed, in order to extract more efficient features from the data. Traditionally, in a ML-ESN, the number of parameters (hidden neurons, sparsity rates, weights) are randomly chosen and manually altered to achieve a minimum learning error. On one hand, this random setting may not guarantee best generalization results. On the other, it can increase the network’s complexity. In this paper, a novel bi-level evolutionary optimization approach is thus proposed for the ML-ESNAE, to deal with these challenges. The first level offers Pareto multi-objective architecture optimization, providing maximum learning accuracy while maintaining a reduced complexity target. Next, every Pareto optimal solution obtained from the first level undergoes a mono-objective weights optimization at the second level. Particle Swarm Optimization (PSO) is used as an evolutionary tool for both levels 1 and 2. An empirical study shows that the evolved ML-ESNAE produces a noticeable improvement in extracting new, more expressive data features from original ones. A number of application case studies, using a range of benchmark datasets, show that the extracted features produce excellent results in terms of classification accuracy. The effectiveness of the evolved ML-ESNAE is demonstrated for both noisy and noise-free data. In conclusion, the evolutionary ML-ESNAE is proposed as a new benchmark for the evolutionary AI and machine learning research community.
Measuring Hotel Review Sentiment: An Aspect-Based Sentiment Analysis Approach

Reviews of travelers regarding different characteristics of a hotel are one of the worthiest sources for the managers to enhance their services, facilities, and marketing campaigns. In finding a way to improve the practical experience of both buy-side and sell-side in the hospitality market, we apply sentiment analysis for hospitality data from a user-generated content site named TripAdvisor. Typically, from big data including both quantitative data and qualitative data of customer's reviews, our contributions are first proposing a framework to utilize big data analysis to identify which aspects/features along with their polarities that customers are focusing, and then inferring and grouping them into 11 topics toward different 405 hotels in Ho Chi Minh City. This study adds more contributions to finding the emerging opinions of customers towards the different topics of hotel reviews by providing an annotated dataset in hotel reviews which ultimately benefits for further research in this field.
Web archives as a data resource for digital scholars

The aim of this article is to provide an exploratory analysis of the landscape of web archiving activities in Europe. Our contribution, based on desk research, and complemented with data from interviews with representatives of European heritage institutions, provides a descriptive overview of the state-of-the-art of national web archiving in Europe. It is written for a broad interdisciplinary audience, including cultural heritage professionals, IT specialists and managers, and humanities and social science researchers. The legal, technical and operational aspects of web archiving and the value of web archives as born-digital primary research resources are both explored. In addition to investigating the organisations involved and the scope of their web archiving programmes, the curatorial aspects of the web archiving process, such as selection of web content, the tools used and the provision of access and discovery services are also considered. Furthermore, general policies related to web archiving programmes are analysed. The article concludes by offering four important issues that digital scholars should consider when using web archives as a historical data source. Whilst recognising that this study was limited to a sample of only nine web archives, this article can nevertheless offer some useful insights into the technical, legal, curatorial and policy-related aspects of web archiving. Finally, this paper could function as a stepping stone for more extensive and qualitative research.
Handling leakage with subsystem codes

Abstract Leakage is a particularly damaging error that occurs when a qubit state falls out of its two-level computational subspace. Compared to independent depolarizing noise, leaked qubits may produce many more configurations of harmful correlated errors during error-correction. In this work, we investigate different local codes in the low-error regime of a leakage gate error model. When restricting to bare-ancilla extraction, we observe that subsystem codes are good candidates for handling leakage, as their locality can limit damaging correlated errors. As a case study, we compare subspace surface codes to the subsystem surface codes introduced by Bravyi et al . In contrast to depolarizing noise, subsystem surface codes outperform same-distance subspace surface codes below error rates as high as ⪅ 7.5 × 10 −4 while offering better per-qubit distance protection. Furthermore, we show that at low to intermediate distances, Bacon–Shor codes offer better per-qubit error protection against leakage in an ion-trap motivated error model below error rates as high as ⪅ 1.2 × 10 −3 . For restricted leakage models, this advantage can be extended to higher distances by relaxing to unverified two-qubit cat state extraction in the surface code. These results highlight an intrinsic benefit of subsystem code locality to error-corrective performance.
A novel recursive T-S fuzzy semantic modeling approach for discrete state-space systems

In this paper, we propose a novel recursive Takagi-Sugeno (T-S) fuzzy semantic modeling approach for discrete state-space system. According to the information learning theoretic (ILT), the correntropy can capture the higher moments of the error probability distribution to deal with non-Gaussian noise. Considering the advantages of fuzzy theory and correntropy, fuzzy correntropy is constructed and a novel kernel fuzzy C-regression model clustering based on fuzzy correntropy is proposed to solve the premise parameter identification problem of the T-S fuzzy model. To the identification of the consequent part parameters of the T-S fuzzy model, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is presented. Moreover, to evaluate the performance of the proposed fuzzy model, the proposed T-S fuzzy model is applied to solve the problem of maneuvering target tracking by incorporating the target feature semantic information. Finally, the experiment results show the proposed algorithm can effectively track a maneuvering target, and its performance is better than the exist algorithms, such as interacting multiple model Kalman filter (IMMKF), interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter the (IMMRBPF).
Diverse adversarial network for image super-resolution

Recently, there is a fast growth in Generative adversarial network and many works have appeared focusing not only images but also videos. Despite of remarkable success of GAN in image super resolution, it suffers from the major problem of poor perceptual quality. While employing a GAN for super resolution, it tends to generate over-smoothed images that lacks high frequency textures and do not look natural. We propose an intuitive generalization to Generative Adversarial Network and its conditional variations to address the problem of image super-resolution and improves the test quality of images. DGAN is a diverse GAN architecture incorporating multiple generators and a single discriminator. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. To enforce that multiple generators produce diverse samples, the discriminator trains a loss function to distinguish between real and fake samples by designed margins, and multiple generators alternately produce realistic samples by minimizing their losses. In fact, this paper addresses 2 main challenges; recovering realistic texture low resolution images and speed up the training process. We perform extensive experiments and compare the proposed model with other variants of GAN to demonstrate the efficiency and stability of the proposed model in both quantitative and qualitative benchmarks.
Virtual Reality: Digital or Fictional?

Abstract Are the objects and events that take place in Virtual Reality genuinely real? Those who answer this question in the affirmative are realists , and those who answer in the negative are irrealists . In this paper we argue against the realist position, as given by Chalmers (2017), and present our own preferred irrealist account of the virtual. We start by disambiguating two potential versions of the realist position—weak and strong— and then go on to argue that neither is plausible. We then introduce a Waltonian variety of ictionalism about the virtual, arguing that this sort of irrealist approach avoids the problems of the realist positions, fits with a unifying theory of representational works, and offers a better account of the phenomenology of engaging in virtual experiences.
A New Steganography Method Using Integer Wavelet Transform and Least Significant Bit Substitution

Abstract Steganography is a data hiding technique, which is used for securing data. Both spatial and transform domains are used to implement a steganography method. In this paper, a novel transform domain method is proposed to provide a better data hiding method. The method uses a multi-resolution transform function, integer wavelet transform (IWT) that decomposes an image into four subbands: low-low, low-high, high-low and high-high subband. The proposed method utilizes only the three subbands keeping the low-low subband untouched which helps to improve the quality of the stego image. The method applies a coefficient value differencing approach to determine the number of secret bits to be embedded in the coefficients. The method shows a good performance in terms of embedding capacity, imperceptibility and robustness. A number of metrics are computed to show the quality of the stego image. It can also withstand RS steganalysis, Chi-squared test and Subtractive Pixel Adjacency Matrix steganalysis successfully. The deformation of the histogram and Pixel Difference Histogram for different embedding percentages are also demonstrated, which show a significant similarity with the original cover image. The proposed method shows an achievement of 2.3bpp embedding capacity with a good quality of stego image.
Contribution study of monogenic wavelets transform to reduce speckle noise in digital speckle pattern interferometry

Wavelets shrinkage is the most illustrative of wavelets transform for speckle noise reduction. We aim to study the performance of a monogenic wavelet transform to reduce the speckle noise in digital speckle pattern interferometric fringes. The proposed method is implemented on simulated and experimental speckle fringe patterns and its performance is appraised on the basis of peak signal-to-noise ratio (PSNR) and quality index (Q). The ability to reduce the speckle noise by the proposed method is compared with other classical speckle denoising methods. The obtained results corroborate the effectiveness of the proposed method for speckle noise reduction in speckle fringes in terms of PSNR and Q. It is also observed that the method provides better qualitative and quantitative results. Furthermore, the proposed method preserves the edge information of the speckle fringes, a feature that is quantified by the edge preservation index.
Introducing the Temporal Dimension to Memory Forensics

Kickstarted by the Digital Forensic Research Workshop (DFRWS) conference in 2005, modern memory analysis is now one of most active areas of computer forensics and it mostly focuses on techniques to locate key operating system data structures and extract high-level information. These techniques work on the assumption that the information inside a memory dump is consistent and the copy of the physical memory was obtained in an atomic operation. Unfortunately, this is seldom the case in real investigations, where software acquisition tools record information while the rest of the system is running. Thus, since the content of the memory is changing very rapidly, the resulting memory dump may contain inconsistent data. While this problem is known, its consequences are unclear and often overlooked. Unfortunately, errors can be very subtle and can affect the results of an analysis in ways that are difficult to detect. In this article, we argue that memory forensics should also consider the time in which each piece of data was acquired. This new temporal dimension provides a preliminary way to assess the reliability of a given result and opens the door to new research directions that can minimize the effect of the acquisition time or detect inconsistencies. To support our hypothesis, we conducted several experiments to show that inconsistencies are very frequent and can negatively impact an analysis. We then discuss modifications we made to popular memory forensic tools to make the temporal dimension explicit during the analysis and to minimize its effect by resorting to a locality-based acquisition.
Innovative Data Management in advanced characterization: Implications for materials design

This paper describes a novel methodology of data documentation in materials characterization, which has as starting point the creation and usage of any Data Management Plan (DMP) for scientific data in the field of materials science and engineering, followed by the development and exploitation of ontologies for the harnessing of data created through experimental techniques. The case study that is discussed here is nanoindentation, a widely used method for the experimental assessment of mechanical properties on a small scale. The new documentation structure for characterization data (CHADA) is based on the definition of (i) sample, (ii) method, (iii) raw data and (iv) data analysis as the main component of the metadata associated to any characterization experiment. In this way, the relevant information can be stored inside the metadata associated to the experiment. The same methodology can be applicable to a large number of techniques that produce big amount of raw data, while at the same time it can be invaluable tool for big data analysis and for the creation of an open innovation environment, where data can be accessed freely and efficiently. Other fundamental aspects are reviewed in the paper, including the taxonomy and curation of data, the creation of ontology and classification of characterization techniques, the harnessing of data in open innovation environments via database construction along with the retrieval of information via algorithms. The issues of harmonization and standardization of such novel approaches are also critically discussed. Finally, the possible implications for nanomaterial design and the potential industrial impact of the new approach are described and a critical outlook is given.
Twitter Sentiment Analysis: A Case Study for Apparel Brands

Social media especially Twitter is providing a space for expression and opinions, where users discuss various events, services, and brands. Entrepreneurs are in continuous need of the feedback about their services to improve the quality and quantity. However, due to the bulk amount of data, it's difficult to detect the consumer's opinions. This article deliberates the problems about the Twitter data for the sentiment analysis. Furthermore, it implements the text mining and document-based sentiment on the preprocessed Twitter data through the machine learning techniques, Naïve Bayes and lexicon dictionary. Our case study is to find the public opinion about the top two apparel international brands and compare the positive and negative attitude of common users about each brand. We found that positive reviews of Adidas are more than the Nike while there is the slight difference in negative reviews. It founds that people want to discuss the other brands as comparisons when they are talking about just one brand.
An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms

In outsourcing computation models, weak devices (clients) increasingly rely on remote servers (workers) for data storage and computations. However, most of these servers are hackable or untrustworthy, which makes their computation questionable. Therefore, there is need for clients to validate the correctness of the results of their outsourced computations and ensure that servers learn nothing about their clients other than the outputs of their computation. In this work, an efficient privacy preservation validation approach is developed which allows clients to store and outsource their computations to servers in a semi-honest model such that servers' computational results could be validated by clients without re-computing the computation. This article employs a morphism approach for the client to efficiently perform the proof of correctness of its outsourced computation without re-computing the whole computation. A traceable pseudonym is employed by clients to enforce anonymity.
Asymmetric controlled bidirectional remote preparation of two- and three-qubit equatorial state

In this paper, a novel asymmetric controlled bidirectional remote preparation scheme is proposed. In our scheme, Alice and Bob are not only the senders but also the receivers with the control of Charlie. By using the eleven-qubit entangled state as the quantum channel, Alice prepares an arbitrary two-qubit equatorial state for Bob and Bob prepares an arbitrary three-qubit equatorial state for Alice simultaneously. Firstly, we give the construction process of the quantum channel. Secondly, the whole recovery operations are given. Alice and Bob can recover the prepared state determinately. Thirdly, we consider the effect of the noisy environment (amplitude-damping and phase-damping) in our scheme and calculate the fidelities of the output states. Finally, since our scheme does not need additional operations and auxiliary qubits, the efficiency of our scheme is higher than that of the previous schemes.
DCWI: Distribution descriptive curve and Cellular automata based Writer Identification

Writer identification is an active area of research owing to its applications in a wide variety of fields, ranging from ancient document analysis to modern forensic document analysis. It deals with the writing style of documents and the learning of the discriminating features of different writers. In the domain of pattern recognition, the extraction of discriminative features of different writers has become very challenging. In order to address this concern, this work highlights a distribution descriptive curve (DDC-) and cellular automata (CA-) based model approach. The DDC utilizes the idea of the pixel distribution of handwritten text images to generate a unique curve as a feature vector. The generated feature vector is then fed to a support vector machine (SVM) as an input to identify the writer. Simultaneously, in a parallel mode, the initial handwritten text images are processed repeatedly with CA to generate another set of feature vectors. This new set of generated feature vectors are fed to a similarity-based classifier (SBC) as an input. The writer is predicted on the basis of the similarity of the features. The results from both of the approaches (DDC + SVM and CA + SBC) are merged to improve the performance of the model. This proposed model, DCWI, has good writer-identification capabilities compare with state-of-the-art techniques. Eventually, writer identification is accomplished using the ranking-based score scheme. The proposed model is evaluated on different datasets, e.g., IAM for English, IFN/ENIT for Arabic, and Kannada and Devanagari (Hindi) for the Indic script. The results show that the proposed model has better performance compared with existing state-of-the-art techniques.
Capturing and measuring thematic relatedness

In this paper we explain the difference between two aspects of semantic relatedness: taxonomic and thematic relations. We notice the lack of evaluation tools for measuring thematic relatedness, identify two datasets that can be recommended as thematic benchmarks, and verify them experimentally. In further experiments, we use these datasets to perform a comprehensive analysis of the performance of an extensive sample of computational models of semantic relatedness, classified according to the sources of information they exploit. We report models that are best at each of the two dimensions of semantic relatedness and those that achieve a good balance between the two.
Factorization-Based Active Contour for Water-Land SAR Image Segmentation via the Fusion of Features

The segmentation of synthetic aperture radar (SAR) water-land images is a very difficult task not only because of strong multiplicative noise but also due to the blurred boundary, irregular shape, and together with diminished contrast. In this paper, we propose a matrix factorization active contour model based on fused features for SAR image segmentation. First, to enhance the robustness, multiple features are utilized. For each pixel location, feature maps (matrix) are constructed by combining wavelet textual features, Gaussian (DoG) filter features, and Gabor filter features via local spectral histogram, which improves spatial pattern and express image structure. Second, the energy function is constructed based on region information and edge information of SAR image. Region information is obtained via matrix factorization theory on the feature matrix. Edge information is obtained by modified the ratio of exponentially weighted averages operator. Then, a convex energy function is proposed to avoid the local minima. A fast dual formulation is introduced for the evolution of the contour. Finally, synthetic and real SAR data are used for verification. The experimental results demonstrate the proposed algorithm is effective for water/land segmentation in SAR images.
Foreign Direct Investment, Political Business Cycles and Welfare in Africa

Abstract We examine the relation between foreign direct investment (FDI), elections and welfare in Africa using data from 1990 to 2014. Our results show evidence that elections play a positive role in altering the relationship between FDI and welfare as they are considered by investors as risk reduction events. Interestingly, when we split the sample based on the quality of institutions, we find that the interaction between FDI and elections is mostly insignificant in countries with strong institutions. However, the interaction between FDI and elections is positive and significant in countries with weak institutions. © 2019 John Wiley &amp; Sons, Ltd.
Resource Deflation

Data centers and clouds are increasingly offering low-cost computational resources in the form of transient virtual machines. Whenever demand for computational resources exceeds their availability, transient resources can reclaimed by preempting the transient VMs. Conventionally, these transient VMs are used by low-priority applications that can tolerate the disruption caused by preemptions.