Reviewerly/exHarmony huggingface dataset:
Neural Predictors’ Accuracy

We examine the performance of different predictors in the deterministic environment and test their robustness against noise. In particular, we mimic the classical case of measurement noise by adding a random Gaussian signal of different intensity to the deterministic output of some archetypal chaotic systems. Then, we examine the critical case of structural noise, represented by the slow variation of the growth rate parameter of the logistic map. In both cases, the presence of noise rapidly degrades all the performance indicators, but, interestingly, the best deterministic predictor, i.e., LSTM trained without teacher forcing, remains the best also in the stochastic and non-stationary environments. Finally, we examine solar irradiance and ozone concentration time series, and again the same predictor turns out to be the best and can also be reliably applied to similar datasets in the same domain (domain adaptation).
A Fuzzy-Based Multi-Criteria Decision-Making Approach for the Selection of Digital Image Forensic Tools

Digital image forensic science is a sub-research area of multimedia security whose objective is to check the authenticity of digital images. Different algorithms as well as tools have been developed to check the forged images. In literature, less attention is given on the evaluation and selection of the digital image forensic tools based on different features like error level analysis, metadata analysis, double joint photographic expert group, etc. Therefore, to address this issue, in this chapter an algorithm has been developed for the selection of the digital image forensic tools based on the ranking values. The ranking values of the digital image forensic tools are computed using TOPSIS method by using the triangular fuzzy numbers. The utilization of the proposed method is discussed with the help of an example in which following tools have been considered during the analysis, i.e., FotoForensics, JPEGsnoop, Forensically, Ghiro, and Izitru.
Wild words

In this paper we analyse the formal and functional ‘extravagance’ of general extenders embedded within morphological schemas, either at the end of coordinate compounds, or as constituents of complex words, especially at the end of lists of complex words formed according to the same schema. After discussing two case studies from Italian (eccetera ‘etcetera’ and tutto ‘everything/all’), we conclude that complex words containing general extenders are ‘wild words’, namely ‘contextuals’ (Clark & Clark 1979) that are created to meet specific communicative needs and are meant not to be stored. As such, they serve as a strategy to create ‘ad hoc categories’ (Mauri & Sansò 2018a). The relationship of ‘wild words’ with lists is also explored and deemed crucial both for their semantic interpretation and for explaining their collocation within morphological structures.
OAuth 2.0

OAuth is an open security standard that enables users to provide specific and time-bound rights to an application to access protected user resources. It stored on some external resource servers without needing them to share their credentials with the application. Unlike websites, for locally installed packaged web applications, the main security challenge is to handle the redirect response. The OAuth flow initiated from packaged web apps is similar to the OAuth flows explained in the current literature. However, for packaged web apps, it is difficult to define an HTTP endpoint as redirection endpoint since these apps are locally installed. The authors have proposed a novel method to execute OAuth flow from such applications with the help of a web runtime framework that manages the life cycle of these applications. They have compared their approach with another two existing approaches. After conducting experiments, they have found their approach blocking all illegal OAuth flow executions. The approach also delivers better OAuth response handling time and power consumption performance.
Visual-Based Intelligence (VBI) Analysis for General Data Protection Regulation (GDPR)

The General Data Protection Regulation (GDPR) is a European data protection regulation that requires both profit organisations (corporates) and not-for-profit organisations to protect personally identifiable information (PII) of UK and European citizens across the world. The organisations can be liable to be fined for not managing the Personally Identifiable Information (PII) such as names, affliations, contact details, bank details and so on. In this paper, we considered email communication data to be analysed and developed visual-based intelligence analysis tool that can visualise and support analysts and decision-makers. Specifically, the nuances in the behaviour of temporal data, PII, understanding unusual behaviours in communication data and what represents a &#x2018;normal&#x2019; and &#x2018;interesting&#x2019; data. The results are quite promising for the organisations to adopt such a tool to manage GDPR in their organisations.
University develops ID system using ear canal

A research team from the US University at Buffalo (UB) have come up with a novel biometric authentication technique, called EarEcho, which identifies people via their ear canal's unique geometry.
Cryptocurrency Price Prediction Using TPU-Based Distributed Machine Learning

The development of blockchain has led to the emergence and widespread use of decentralized cryptocurrencies around the globe. As of 2021, the global market capitalization of cryptocurrencies has crossed two trillion dollars. With increasing popularity and adoption, investors have begun to see cryptocurrencies as an alternative to conventional financial assets. However, the volatility associated with cryptocurrencies makes them a highly risky investment. This gives rise to the need for accurate and efficient price prediction models which can help reduce risks associated with cryptocurrency investments. The model aims at predicting the price of two popular cryptocurrencies: Bitcoin and Ethereum. Tensor processing unit (TPU) is used for providing a distributed environment for the proposed model. The results show that the distributed TPU-trained model performed significantly better than the conventional CPU-trained model in terms of training time while maintaining a high degree of accuracy.
Multi-Feature Fusion based Image Steganography using GAN

In order to solve the problem of information loss, some image steganography methods utilize generative adversarial networks (GANs), while the existing methods can not capture both texture information and semantic features. In this paper, a more accurate image steganography method is proposed, where a multi-level feature fusion procedure based on GAN is designed. Firstly, convolution and pooling operations are added to the network for feature extraction. Then, short links are used to fuse multi-level feature information. Finally, the stego image is generated by confrontation learning between discriminator and generator. Experimental results show that the proposed method has higher steganalysis security under the detection of high-dimensional feature steganalysis and neural network steganalysis. Comprehensive experiments show that the performance of the proposed method is better than ASDL-GAN and UT-GAN.
A Pareto front based methodology to better assess medical image registration algorithms

Non-linear registration models optimize two conflicting objectives, a content-matching term and a deformation smoothness measure. As the desired smoothness regime is problem-specific, there is a need to better compare generic registration algorithms across different smoothness regimes. We propose to compare registration algorithms by estimating their content-matching vs deformation smoothness Pareto front. Specifically, we assess the deformation smoothness level reached by each algorithm at different content-matching levels. We introduce a new objective function to sample the Pareto front along a specific iso-content-matching line. We demonstrate the applicability of our method on chest-CT inter-patient registration by comparing 5 learning-based registration algorithms.
Video-based tracking of fishes in the Lisbon Oceanarium

There are many challenges on tracking fishes from videos taken from single cameras outside fish tanks, such as frequent occlusion of the targets, the presence of schools, high visual similarities between different individuals, even from different species, sudden changes in motions, and very different light conditions. This paper presents our solution system that detects and tracks fish of different species in tanks in the Oceanário de Lisboa. We make a thorough evaluation of several state-of-the-art detectors in those types of scenarios and improve on previous work addressing detection-tracking-classification on the same scenario. Here we show how the tracking methodology was improved, replacing greedy data association and better tuning of the components. Additionally, we introduce a new step of contrast equalisation to mitigate the challenging illumination conditions of the main tank scenario
Semantic Network Interpretation

Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpretation at both filter and decision level. For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes. The decision-level interpretation is achieved by textual summarization that generates an explanatory sentence containing clues behind a network’s decision. A Bayesian inference algorithm is proposed to automatically associate filters and network decisions with visual attributes. Human study confirms that the semantic interpretation is a beneficial alternative or complement to visualization methods. We demonstrate the crucial role that semantic network interpretation can play in understanding a network’s failure patterns. More importantly, semantic network interpretation enables a better understanding of the correlation between a model’s performance and its distribution metrics like filter selectivity and concept sparseness.
Avoidance of operational sampling errors in drinking water analysis

Abstract The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.
Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series

In this paper we address the problem of modelling time series with irregular intervals by incorporating a continuous-time version of the Kalman filter into a neural network architecture. Building on the idea of Recurrent Kalman Networks (RKNs) we use an encoder-decoder structure to learn a latent observation space and latent state space in which the dynamics of the data can be approximated linearly. Here, a recurrent Kalman component alternates between continuous latent state propagation and Bayesian updates from incoming observations. This allows us to model and react instantaneously to observations as they come at arbitrary time steps while ensuring sufficient expressive power to model nonlinear dynamics. Experiments on synthetic data show that the model is indeed able to capture continuous, nonlinear dynamics.
A Blockchain-based Trusted Testing System of Electric Power Materials

In order to curb the illegal activities in power resources detection, improve the credibility and contribution rate of the industry, and promote the development of high-quality services, this paper proposes a secure and reliable trusted testing system of electric power materials based on blockchain. Firstly, a device and personal information query authorization mechanism is established to provide solutions for personnel and testing equipment authorization. It can help ensure the reliability of testing data on the premise of security. Secondly, we propose a method to deal with the difficulties of testing information management. Lastly, we introduce the case of electricity management helping the power authorities to supervise effectively and increasing the credibility of power material procurement evidence to prove the feasibility of this system.
A Reconfigurable Metadevice for Solving Equations and Inverting Matrices at RF Frequencies

Here, we describe some of the basic steps for solving mathematical equations with a metadevice, i.e., a subwavelength network that allows for the inversion of any desired matrix. The device consists of a connected network architecture of an elementary module. We examine possible RF implementations and provide simulated and experimentally measured preliminary results of this module, and demonstrate the case of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 \times 2$</tex> matrix multiplication, paving the way towards the experimental implementation of metastructures with equation solving and matrix inversion capabilities.
Poetics in hybrid art

This paper comments the process of creating a hybrid art installation, whose version, with the creative coding and interactive video, resulted in the artwork Tela Transeunte (2020). Therefore, it presents a brief reflection about the new media art and the hybrid art. Likewise, stands out the interdisciplinary, collaborative and shared mode of investigation, in addition to the use of open processes and codes, repositories and databases. Practices that operate between art, science and technology, also contributing to rupture the institutionalized systems and for reflection on artistic production.
Accuracy Analysis of an Oblique Underwater Laser Lightsheet Triangulation System

Abstract Laser lightsheet triangulation is a well-established optical measurement method, which is frequently used in industrial applications. With some adaptions, the technique can also be used underwater. Placing a green laser line projector and a camera at a fixed base inside a watertight housing enables flexible and accurate underwater measurements at low cost. To achieve near-orthogonal intersections on the object surface, camera and laser need to be placed oblique to the housing interface. Refraction influences have to be considered strictly in geometric modelling, with the light propagating through the air–glass and glass–water interfaces. The measurement and calibration methods presented in the paper utilize a concept of splitting the lightsheet into multiple sub-beams, which are traced, refracted, and intersected with image observations to receive 3D measurements. In a calibration step, the relative orientation between camera, laser and interfaces is determined. In a theoretical accuracy analysis, the error influences caused by the calibrated parameters is estimated with a standard deviation of 0.2 to 0.4 mm in a depth range of up to 15 cm. A prototype triangulation sensor is applied for practical measurements of two test objects with known geometry. The predicted accuracy is validated in stationary single scan measurements. The sensor is furthermore moved along the test objects, using a six-degrees-of-freedom method to determine its exterior orientation. The combination of multiple single scans results in dense point clouds with 0.3 mm standard deviation compared to a reference.
Efficient Quantization Techniques for Deep Neural Networks

As model prediction becomes more and more accurate and the network becomes deeper and deeper, the amount of memory consumed by the neural network becomes a problem, especially on mobile devices. It is also very difficult to balance the tradeoff between computational cost and battery life, which makes mobile devices very hard as well to become smarter. Model quantification techniques provide the opportunity to tackle this tradeoff by reducing the memory bandwidth and storage and improving the system throughput and latency. This paper discusses and compares the state-of-the-art methods of neural network quantification methodologies including Post Training Quantization (PTQ) and Quantization Aware Training (QAT). PTQ directly quantizes the trained floating-point model. The implementation process is simple and does not require quantization during the training phase. QAT requires us to use simulated quantization operations to model the effect of the quantization, and forward and backward passes are usually performed in the floating-point model. Finally, as discussed in the experiments in this paper, we conclude that with the evolution of the quantization techniques, the accuracy gap between PTQ and QAT is shrinking.
Periphrases and irregular paradigms in Italo-Romance

Morphomes constitute autonomous distributional patterns that are purely morphological and internal to the morphological component. These patterns often originate as the accidental product of sound change but are subsequently preserved and replicated in diachrony in a way that suggests that they have become cognitive templates or abstract generalizations for speakers. Morphomes are not confined to synthetic structures but extend to other phenomena such as periphrastic constructions of various types. The chapter shows that in southern Italo-Romance, morphomic distributions have analogues within grammaticalized periphrases, including motion and progressive periphrases with double inflexion, as well as modal periphrases. Crucially, a necessary condition for a periphrastic paradigm to be morphomically modelled is grammaticalization. Indeed, the morphophonological changes that are typically described in terms of grammaticalization and morphologization provide the conditions for a morphomic attraction to a recurrent pattern of irregularity.
Bayesian censored piecewise regression mixture models with skewness

This paper presents censored mixture regression models with piecewise growth curves for assessing longitudinal data that exhibit multiphasic features. Such features may include censoring, skewness, measurement errors in covariates, and mixtures of unobserved subpopulations. In the process of describing those features, identification of differential effects of predictors on a response variable for a heterogeneous population (subpopulations) has recently been highly sought. Regression mixture models are key methods for assessing differential effects of predictors. In this article, we extend regression mixture models with normal distribution to incorporate (i) skew-normal distribution, (ii) left-censoring, (iii) measurement errors, and (iv) piecewise growth mixture modeling for describing multiphasic trajectories over time where the observed observations come from a mixture of unobserved subgroups. The proposed methods are illustrated using real data from an AIDS clinical study and a Bayesian approach.
Role of Blockchain Technology in Building Transparent Supply Chain Management

The chapter's fundamental goal is to discover and feature the job of blockchain technology in inventory networks including its benefits and impediments. The idea of the examination depends on auxiliary information and data. The necessary information and data have been gathered from different sites, magazines, and media reports. Supply affixes the need to confront difficulties as far as quality, cost, and speed. These boundaries can be accomplished effectively with blockchain in the inventory network of the executives.
Augmented Reality and Historical Sites: A Systematic Literature Review

The combination of Augmented Reality (AR) technology and the preservation of historical heritage can promote advances in management, maintenance, and documentation of objects and historical sites.The importance of this integration is that digitalizing these historical heritage assets is a worldwide trend because it is the other way of storing information and allowing access and interaction with the public.This article presents a Systematic Literature Review (SLR), and it aims to identify and analyze existing studies on AR applications used in visualizing historical heritage.A methodological procedure was adopted involving the use of protocols and documents for conducting the SLR.The application areas, specific activities, modeling programs, AR programs, tracking techniques, and visualization devices were identified.Results present which programs, visualization devices and tracking techniques are highlighted in the studies.AR applications used in the sample aim for memory recovery, preservation, and interaction with the historical-cultural heritage.
Measuring Representation of Race, Gender, and Age in Children’s Books: Face Detection and Feature Classification in Illustrated Images

Images in children’s books convey messages about society and the roles that people play in it. Understanding these messages requires systematic measurement of who is represented. Computer vision face detection tools can provide such measurements; however, state-of-the-art face detection models were trained with photographs, and 80% of images in children’s books are illustrated; thus existing methods both misclassify and miss classifying many faces. In this paper, we introduce a new approach to analyze images using AI tools, resulting in data that can assess representation of race, gender, and age in both illustrations and photographs in children’s books. We make four primary contributions to the fields of deep learning and social sciences: (1) We curate an original face detection data set (IllusFace 1.0) by manually labeling 5,403 illustrated faces with bounding boxes. (2) We train two AutoML-based face detection models for illustrations: (i) using IllusFace 1.0 (FDAI); (ii) using iCartoon, a publicly available data set (FDAI iC), each optimized for illustrated images, detecting 2.5 times more faces in our testing data than the established face detector using Google Vision (FDGV). (3) We curate a data set of the race, gender, and age of 980 faces manually labeled by three different raters (CBFeatures 1.0). (4) We train an AutoML feature classification model (FCA) using CBFeatures 1.0. We compare FCA with the performance of another AutoML model that we trained on UTKFace, a public data set (FCA UTK) and of an established model using FairFace (FCF). Finally, we examine distributions of character identities over the last century across the models. We find that FCA is 34% more accurate than FCF in its race predictions. These contributions provide tools to educators, caregivers, and curriculum developers to assess the representation contained in children’s content.