Reviewerly/exHarmony huggingface dataset:
Tracking News Stories Using Blockchain to Guarantee their Traceability and Information Analysis

Nowadays, having a mechanism to guarantee the traceability of the information and to monitor the evolution of the news from its origin, and having elements to know the reputation and credibility of the media, analyze the news as well as its evolution and possible manipulation, etc. is becoming increasingly significant.Transparency in journalism is currently a key element in performing serious and rigorous journalism.End-users and factchecking agencies need to be able to check and verify the information published in different media.This transparency principle enables the tracking of news stories and allows direct access to the source of essential content to contrast the information it contains and to know whether it has been manipulated.Additionally, the traceability of news constitutes another instrument in the fight against the lack of credibility, the manipulation of information, misinformation campaigns and the propagation of fake news.This article aims to show how to use Blockchain to facilitate the tracking and traceability of news so that it can provide support to the automatic indexing and extraction of relevant information from newspaper articles to facilitate the monitoring of the news story and allows users to verify the veracity of what they are reading.
Robust Transparency Against Model Inversion Attacks

Transparency has become a critical need in machine learning (ML) applications. Designing transparent ML models helps increase trust, ensure accountability, and scrutinize fairness. Some organizations may opt-out of transparency to protect individuals’ privacy. Therefore, there is a great demand for transparency models that consider both privacy and security risks. Such transparency models can motivate organizations to improve their credibility by making the ML-based decision-making process comprehensible to end-users. Differential privacy (DP) provides an important technique to disclose information while protecting individual privacy. However, it has been shown that DP alone cannot prevent certain types of privacy attacks against disclosed ML models. DP with low <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> values can provide high privacy guarantees, but may result in significantly weaker ML models in terms of accuracy. On the other hand, setting <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> value too high may lead to successful privacy attacks. This raises the question whether we can disclose accurate transparent ML models while preserving privacy. In this article we introduce a novel technique that complements DP to ensure model transparency and accuracy while being robust against model inversion attacks. We show that combining the proposed technique with DP provide highly transparent and accurate ML models while preserving privacy against model inversion attacks.
Stegomalware detection through structural analysis of media files

The growing diffusion of malware is causing non-negligible economic and social costs. Unfortunately, modern attacks evolve and adapt to defensive mechanisms, and many threats are designed for the optimal exploitation of the traits of the victims. Thus, phenomena such as mobile malware, fileless malware or stegomalware are becoming widespread and represent the next variations of malicious attacks that have to be faced. In particular, the massive amount of digital content shared on the Internet is increasingly more often being used by attackers for the injection of malicious code to bypass security tools or prevent detection.
dPHI: An improved high-speed network-layer anonymity protocol

Abstract The Internet infrastructure has not been built with security or privacy in mind. As a result, an adversary who has control over a single Autonomous System can set-up mass surveillance systems to gather meta data by passively collecting the headers of the messages they route. To solve this problem, lightweight anonymous routing protocols such as LAP, DOVETAIL and most recently PHI have been proposed which are efficient enough to be deployed in a large scale infrastructure such as the Internet. In this paper we take a closer look at PHI and introduce several de-anonymization attacks malicious nodes can perform to reduce the sender and receiver anonymity. As a direct consequence of this analysis we propose a new protocol called dependable PHI (dPHI). The security analysis of dPHI includes a detailed quantitative anonymity analysis that compares dPHI with PHI, LAP and HORNET. Together with the performance analysis, this allows for a good comparison of trade-offs for these anonymity protocols.
Generative adversarial network-based atmospheric scattering model for image dehazing

This paper presents a trainable Generative Adversarial Network (GAN)-based end-to-end system for image dehazing, which is named the DehazeGAN. DehazeGAN can be used for edge computing-based applications, such as roadside monitoring. It adopts two networks: one is generator (G), and the other is discriminator (D). The G adopts the U-Net architecture, whose layers are particularly designed to incorporate the atmospheric scattering model of image dehazing. By using a reformulated atmospheric scattering model, the weights of the generator network are initialized by the coarse transmission map, and the biases are adaptively adjusted by using the previous round’s trained weights. Since the details may be blurry after the fog is removed, the contrast loss is added to enhance the visibility actively. Aside from the typical GAN adversarial loss, the pixel-wise Mean Square Error (MSE) loss, the contrast loss and the dark channel loss are introduced into the generator loss function. Extensive experiments on benchmark images, the results of which are compared with those of several state-of-the-art methods, demonstrate that the proposed DehazeGAN performs better and is more effective.
Leveraging the power of quantum computing for breaking RSA encryption

Encryption is the process of securing confidential data that bars a third party's access to the information.RSA encryption utilises the property of complexity classes wherein the problem of prime integer factorization lies inside the Non-Polynomial time (NP-Hard) class, which makes it impervious to classical computers. Since it is so hard to break even for a computer, it becomes important to do encryption for all the secure transactions. Although it lies outside the capabilities of traditional computing, the recent developments in the field of quantum computing can be utilised to break RSA Encryption. The approach involves mapping of qubits used in a quantum machine to a constraint satisfaction problem (CSP) and then using them to check for factors. This consists of the use of a Multiplicative Boolean circuit in which the qubits utilised by the machine replaces the variables. These Qubits are then mapped as per the gates involved, and the factorization problem is thus transformed into a CSP problem, through which, the factors can be easily found. Once known, these factors can be used to calculate the public and private keys effectively breaking the encryption security. We provide a novel approach to highlight the importance of developing Post-Quantum cryptography techniques for providing a secure channel of communication.
Feature selection based on distance correlation: a filter algorithm

Feature selection (FS) is one of the most powerful techniques to cope with the curse of dimensionality. In the study, a new filter approach to feature selection based on distance correlation is presented (DCFS, for short), which keeps the model-free advantage without any pre-specified parameters. Our method consists of two steps: hard step (forward selection) and soft step (backward selection). In the hard step, two types of associations, between univariate feature and the classes and between group feature and the classes, are involved to pick out the most relevant features with respect to the target classes. Due to the strict screening condition in the first step, some of the useful features are likely removed. Therefore, in the soft step, a feature-relationship gain (like feature score) based on the distance correlation is introduced, which is concerned with five kinds of associations. We sort the feature gain values and implement the backward selection procedure until the errors stop declining. The simulation results show that our method becomes more competitive on several datasets compared with some of the representative feature selection methods based on several classification models.
Research data management policies in USA, UK and Australia universities: An online survey

There has been an increase in demand for research data management (RDM) policies to improve the quality of research data however, there is no clear-cut policy content to guide the process. The purpose of this study is to identify the existing RDM policies. Specifically, the study compares and differentiates the RDM policies in three developed nations (USA, UK and Australia) to ascertain how the policies vary. The RDM policies of 100 universities from the three countries, that are present online were retrieved and content analysis approach using NVivo and SPSS were performed. The results from the analysis revealed that the common underlying facts of the policies were found in the areas of access, retention, sharing, storage and ownership. All the universities share the same core values in the management of their research. They exhibit that Data Management Plan (DMP) is essential. The study concluded that the differences in the data management are mostly issues of focus areas. More so, there is no fixed retention period for research data. To resolve the few differences identified, common criteria for data management is proposed for policy considerations to ensure compliance. Finding from the study is significant to developing countries as it adds to the discourse on data management policies. The study will also enable policy makers in developing countries to draw empirical evidence from the developed world on RDM and this will form a basis for policy direction.
Eye Gaze Based 3D Triangulation for Robotic Bionic Eyes

Three-dimensional (3D) triangulation based on active binocular vision has increasing amounts of applications in computer vision and robotics. An active binocular vision system with non-fixed cameras needs to calibrate the stereo extrinsic parameters online to perform 3D triangulation. However, the accuracy of stereo extrinsic parameters and disparity have a significant impact on 3D triangulation precision. We propose a novel eye gaze based 3D triangulation method that does not use stereo extrinsic parameters directly in order to reduce the impact. Instead, we drive both cameras to gaze at a 3D spatial point P at the optical center through visual servoing. Subsequently, we can obtain the 3D coordinates of P through the intersection of the two optical axes of both cameras. We have performed experiments to compare with previous disparity based work, named the integrated two-pose calibration (ITPC) method, using our robotic bionic eyes. The experiments show that our method achieves comparable results with ITPC.
Teaching Students Software Architecture Decision Making

Making the right decisions is challenging for architects on all levels of seniority. Less experienced architects in particular perceive the transition from design problems to their solutions as hard; it is not always clear how to find suitable concepts and technologies, how to compare alternatives, and how to build consensus. Lack of experience makes it difficult to train software engineering students in the identification, selection, and collective evaluation of design alternatives. Moreover, human factors such as cognitive bias make “soft” topics like architecture decisions rather hard to teach. To overcome these issues and let students gain the required experience, a Spanish University ran two experiments. Undergraduate computer science students assumed different roles in collaborative decision-making tasks and design activities. They used a novel decision-modeling tool to capture and challenge the relevant design decisions. This paper describes this new teaching setup and reports on lessons learned.
Research on Credit Risk Measurement of Small and Micro Enterprises Based on the Integrated Algorithm of Improved GSO and ELM

Small and micro enterprises play a very important role in economic growth, technological innovation, employment and social stability etc. Due to the lack of credible financial statements and reliable business records of small and micro enterprises, they are facing financing difficulties, which has become an important factor hindering the development of small and micro enterprises. Therefore, a credit risk measurement model based on the integrated algorithm of improved GSO (Glowworm Swarm Optimization) and ELM (Extreme Learning Machine) is proposed in this paper. First of all, according to the growth and development characteristics of small and micro enterprises in the big data environment, the formation mechanism of credit risk of small and micro enterprises is analyzed from the perspective of granularity scaling, cross-border association and global view driven by big data, and the index system of credit comprehensive measurement is established by summarizing and analyzing the factors that affect the credit evaluation index. Secondly, a new algorithm based on the parallel integration of the good point set adaptive glowworm swarm optimization algorithm and the Extreme learning machine is built. Finally, the integrated algorithm based on improved GSO and ELM is applied to the credit risk measurement modeling of small and micro enterprises, and some sample data of small and micro enterprises in China are collected, and simulation experiments are carried out with the help of MATLAB software tools. The experimental results show that the model is effective, feasible, and accurate. The research results of this paper provide a reference for solving the credit risk measurement problem of small and micro enterprises and also lay a solid foundation for the theoretical research of credit risk management.
An Uplink Communication-Efficient Approach to Featurewise Distributed Sparse Optimization With Differential Privacy

In sparse empirical risk minimization (ERM) models, when sensitive personal data are used, e.g., genetic, healthcare, and financial data, it is crucial to preserve the differential privacy (DP) in training. In many applications, the information (i.e., features) of an individual is held by different organizations, which give rise to the prevalent yet challenging setting of the featurewise distributed multiparty model training. Such a setting is also beneficial to the scalability when the number of features exceeds the computation and storage capacity of a single node. However, existing private sparse optimizations are limited to centralized and samplewise distributed datasets only. In this article, we develop a differentially private algorithm for the sparse ERM model training under the featurewise distributed datasets setting. Our algorithm comes with guaranteed DP, nearly optimal utility, and reduced uplink communication complexity. Accordingly, we present a more generalized convergence analysis for block-coordinate Frank–Wolfe (BCFW) under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arbitrary sampling</i> (denoted as BCFW-AS in short), which significantly extends the known convergence results that apply to two specific sampling distributions only. To further reduce the uplink communication cost, we design an active private feature sharing scheme, which is new in both design and analysis of BCFW, to guarantee the convergence of communicating Johnson–Lindenstrauss transformed features. Empirical studies justify the new convergence as well as the nearly optimal utility theoretical results.
A two-step phase-shifting algorithm dedicated to fringe projection profilometry

Improving the time efficiency of fringe projection profilometry (FPP) is an attractive problem. For FPP using phase-shifting, it is desired to improve the efficiency by reducing the step number for phase retrieval. This paper proposes a two-step phase-shifting algorithm dedicated to FPP. Considering the physical process of FPP, the captured fringe image is formulated with two variables, i.e. surface reflectance and phase value. And a phase shift is introduced to get the two equations, which lead to the close-form solution for phase calculation. Then the phase error due to ambient light is analyzed via a line-circle model, and an algorithm of refining the phase calculation is proposed based on the estimation of the actual fringe contrast. The validity of the proposed approach is demonstrated with experiments.
Cosmo VAE: Variational Autoencoder for CMB Image Inpainting

Cosmic microwave background radiation (CMB) is critical to the understanding of the early universe and precise estimation of cosmological constants. Due to the contamination of thermal dust noise in the galaxy, the CMB map that is an image on the two-dimensional sphere has missing observations, mainly concentrated on the equatorial region. The noise of the CMB map has a significant impact on the estimation precision for cosmological parameters. Inpainting the CMB map can effectively reduce the uncertainty of parametric estimation. In this paper, we propose a deep learning-based variational autoencoder - CosmoVAE, to restoring the missing observations of the CMB map. The input and output of CosmoVAE are square images. To generate training, validation, and test data sets, we segment the full-sky CMB map into many small images by Cartesian projection. CosmoVAE assigns physical quantities to the parameters of the VAE network by using Fourier coefficients, which are sampled by the angular power spectrum of the Gaussian random field as latent variables. CosmoVAE adopts a new loss function to improve the learning performance of the model, which consists of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> reconstruction loss, Kullback-Leibler divergence between the posterior distribution of encoder network and the prior distribution of latent variables, perceptual loss, and total-variation regularizer. The proposed model achieves state of the art performance for Planck Commander 2018 CMB map inpainting.
iNALU: Improved Neural Arithmetic Logic Unit

Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.
Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval

This paper presents an information fusion method for the automatic classification and retrieval of prostate histopathology whole-slide images (WSIs). The approach employs a weakly-supervised machine learning model that combines a bag-of-features representation, kernel methods, and deep learning. The primary purpose of the method is to incorporate text information during the model training to enrich the representation of the images. It automatically learns an alignment of the visual and textual space since each modality has different statistical properties. This alignment enriches the visual representation with complementary semantic information extracted from the text modality. The method was evaluated in both classification and retrieval tasks over a dataset of 235 prostate WSIs with their pathology report from the TCGA-PRAD dataset. The results show that the multimodal-enhanced model outperform unimodal models in both classification and retrieval. It outperforms state–of–the–art baselines by an improvement in WSI cancer detection of 4.74\(\%\) achieving 77.01\(\%\) in accuracy, and an improvement of 19.35\(\%\) for the task of retrieving similar cases, obtaining 64.50\(\%\) in mean average precision.
Simultaneous task allocation and motion scheduling for complex tasks executed by multiple robots

The coordination of multiple robots operating simultaneously in the same workspace requires the integration of task allocation and motion scheduling. We focus on tasks in which the robot's actions are not confined to small volumes, but can also occupy a large time-varying portion of the workspace, such as in welding along a line. The optimization of such tasks presents a considerable challenge mainly due to the fact that different variants of task execution exist, for instance, there can be multiple starting points of lines or closed curves, differentfilling patterns of areas, etc. We propose a generic and computationally efficient optimization method which is based on constraint programming. It takes into account the kinematics of the robots and guarantees that the motions of the robots are collision-free while minimizing the overall makespan. We evaluate our approach on several use-cases of varying complexity: cutting, additive manufacturing, spot welding, inserting and tightening bolts, performed by a dual-arm robot. In terms of the makespan, the result is superior to task execution by one robot arm as well as by two arms not working simultaneously.
Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method

Determining the polarities of words in a given context has been in existence since the inception of computational linguistics, text mining, and sentiment analysis. Due to its fundamental role in determining the overall semantic orientation of natural language expressions, it is considered one of the most challenging issues facing these areas of research. This paper introduces a new implementation of the lexicon-based word polarity identification method on several customer reviews datasets. Herein, we use a variation of a lexicon-based word polarity identification method that operates by computing the semantic relatedness between the context expansion set of the target word and a synonym expansion set comprising the synonyms of all words surrounding the target word within the original text fragment. The polarity of the target word is determined as that for which the semantic relatedness between these two meaningful sets is the highest. Unlike most existing lexicon-based multi-polarity word identification methods, the used method is not based on estimating pairwise relatedness at term-level, but instead, it is based on measuring semantic relatedness at the fragment-level. This enables the exploration and capture of a higher degree of semantic and sentimental information, and is more consistent with people' understanding through the consideration of the larger context in which the word appears. Its performance can be further improved by incorporating an initial step in which the relative negation scope of words in the given text fragment is managed while determining their sentiment orientation. The implementation results demonstrate that the used variation of the lexicon-based word polarity identification method performs favourably against compared methods, as evaluated on numerous benchmark datasets through stand-alone and end-to-end evaluation models.