Reviewerly/exHarmony huggingface dataset:
Data Augmentation of Neonatal Thermal Images Using Deep Learning

Obtaining neonatal thermal image data is a pricey expedition. The situations of both patients and hospitals may not be in favor of acquiring thermal images of neonates. This will leave the dataset to be an ill-representation of reality which affects the learning capabilities of algorithms. Data augmentation is one of the widely used techniques to create data that can be used when we have an imbalanced dataset or sensitive data points. Data Augmentation can be done using image translation, histogram equalization, zooming, rotation, flip, and scale called traditional methods. The diversity gained by making use of traditional modifications are relatively small when compared to DCGAN. Even though an effort is made to increase the size of the dataset using traditional augmentation methods the resultant images are still the copy of their original. We make use of DCGAN to understand and create thermal images which resemble original images yet diverse from original neonatal thermal images.
Log-ratio transformations for dietary compositions: numerical and conceptual questions

Abstract When evaluating the impact of macronutrient intakes on health outcomes, researchers in nutritional epidemiology are mostly interested in two types of information: the relative importance of the individual macronutrients and the absolute effect of total energy intake. However, the usual substitution models do not allow these separate effects to be disentangled. Dietary data are typical examples of compositional data, which convey relative information and are, therefore, meaningfully expressed in the form of ratios. Various formulations of log-ratios have been proposed as a means of analysing compositional data, and their interrelationships when they are used as predictors in regression models have been previously reported. This note describes the application of distinct log-ratio transformations to the composition of dietary macronutrients and discusses the interpretative implications of using them as explanatory variables in regression models together with a term for the total composition (total energy intake). It also provides examples that consider serum glucose levels as the health outcome and are based on data coming from an Italian population-based study. The log-ratio transformation of dietary data has both numerical and conceptual advantages, and overcomes the drawbacks of traditional substitution models.
Parallel/Distributed Intelligent Hyperparameters Search for Generative Artificial Neural Networks

This article presents a parallel/distributed methodology for the intelligent search of the hyperparameters configuration for generative artificial neural networks (GANs). Finding the configuration that best fits a GAN for a specific problem is challenging because GANs simultaneously train two deep neural networks. Thus, in general, GANs have more configuration parameters than other deep learning methods. The proposed system applies the iterated racing approach taking advantage of parallel/distributed computing for the efficient use of resources for configuration. The main results of the experimental evaluation performed on the MNIST dataset showed that the parallel system is able to efficiently use the GPU, achieving a high level of parallelism and reducing the computational wall clock time by 78%, while providing competitive comparable results to the sequential hyperparameters search.
A Novel Multiband Remote-Sensing Image Encryption Algorithm Based on Dual-Channel Key Transmission Model

With the rapid development of remote sensing technology, satellite remote sensing images have been involved in many areas of people’s lives. Remote sensing images contain military secrets, land profiles, and other sensitive data, so it is urgent to encrypt remote sensing images. This paper proposes a dual-channel key transmission model. The plaintext related key is embedded into the ciphertext image through bit-level key hiding transmission strategy, which enhanced the ability of ciphertext image to resist known-plaintext attack and chosen plaintext attack. In addition, a multiband remote sensing image encryption algorithm based on Boolean cross-scrambling and semi-tensor product diffusion is designed. Firstly, the pixel positions of each band of the remote sensing image are disturbed. Then, the random sequence generated by the four-dimensional chaotic system is processed and deformed to obtain a Boolean matrix. Based on the generated Boolean matrix and certain rules, the cross-confusion between the bands is carried out. Finally, the semi-tensor product operation is used in the diffusion process. Simulation results and experimental analysis show that the proposed algorithm obtains a larger key space and has stronger antiattack ability than other remote sensing image encryption algorithms. It can meet the security transmission of multiband remote sensing image in open space.
MRSA: A High-Efficiency Multi ROMix Scrypt Accelerator for Cryptocurrency Mining and Data Security

The development of low-energy, high-performance hardware for cryptocurrency mining is gaining widespread attention.The mining process for proof-of-work (PoW) in conventional cryptocurrencies' blockchains is increasingly being replaced by application-specific integrated circuits (ASICs).This leads to many security threats for the blockchain network because it decreases security and increases power consumption for mining.Therefore, Scrypt, the most representative ASIC-resistant algorithm, was developed to solve this problem.However, there are still some problems and challenges with the current Scrypt hardware.This article presents a new hardware architecture for the Scrypt algorithm intended for a PoW-based cryptocurrency mining system.The proposed Multi ROMix Scrypt Accelerator (MRSA) hardware architecture applies several optimization techniques: configuration, local-memory computing with high-performance pipelined Multi ROMix and rescheduling resources to significantly increase processing speed, flexibility, and energy efficiency.For evaluation, the MRSA is implemented on field-programmable gate arrays (FPGAs) to examine its actual performance, consumption, and correctness.Evaluation results on a Xilinx system-on-chip (SoC) with the ALVEO U280 Data Center Accelerator Card FPGA show that the MRSA is much more power-efficient than some of the most powerful commercial CPUs, GPUs, and other FPGA implementations.On the ALVEO U280, the MRSA achieves a maximum hash rate of 296.76 kHash/s, a throughput of 304.9 Mbps when reaching a maximum frequency of 259.94 MHz, and a power consumption of 18.12 W. The energy efficiency of the MRSA on the ALVEO U280 SoC is 52.83 and 867.88 times higher than those on an RTX 3090 GPU and an i9-10940X CPU, respectively.
Prior Knowledge Elicitation: The Past, Present, and Future

Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. In principle, prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem. In practice, however, we are still fairly far from having usable prior elicitation tools that could significantly influence the way we build probabilistic models in academia and industry. We lack elicitation methods that integrate well into the Bayesian workflow and perform elicitation efficiently in terms of costs of time and effort. We even lack a comprehensive theoretical framework for understanding different facets of the prior elicitation problem. Why are we not widely using prior elicitation? We analyse the state of the art by identifying a range of key aspects of prior knowledge elicitation, from properties of the modelling task and the nature of the priors to the form of interaction with the expert. The existing prior elicitation literature is reviewed and categorized in these terms. This allows recognizing under-studied directions in prior elicitation research, finally leading to a proposal of several new avenues to improve prior elicitation methodology.
TOWARDS AUGMENTED REALITY TECHNOLOGY IN CAD/CAM SYSTEMS AND ENGINEERING EDUCATION

Computer-aided design and manufacturing is related to usage of a wide variety of technologies for performance of engineering tasks like: analysis, planning, design, simulation, diagnostics, optimization, producing. The integration of CAD /CAM and AR system play an important role in achieving proper understanding of the 2D and 3D graphical objects modelling. This unity becomes a fundamental part of a standard design process. The aim of the paper is to summarize and analyse existing innovative solutions in utilization of augmented reality in computer-aided engineering practice and received knowledge to be used in the courses from Higher education. Also, the exploration about augmented reality usage in teaching, learning and assessment in university courses is performed in order to be chosen technology solution for delivering the knowledge in the context of CAD/CAM systems. The research method for review performing is based on examination of scientific papers indexed in SCOPUS and Web of Science, indexed by search engines Google Scholar, Google Search and Semantic Scholar. The keywords augmented reality, CAD, CAM, product design, product manufacturing, engineering, are used for forming several searching queries, investigating the topics concerning AR in CAD/CAM systems. A conceptual meta-model outlining the utilization of augmented reality technology in CAD/CAM systems and application of this knowledge for engineering education is developed. It consists of two parts. The first part describes the AR technology usage in CAD/CAM engineering process that includes several stages in design and manufacturing of products. It can be said that many of the stages are supported by AR for documentation, presentation and experimentation. The second part explains the utilization of AR technology in engineering education. An AR-based eLearning system should possess features for teaching, learning and assessment through usage of AR web-based, mobile-based or combined technologies. The summarized knowledge about facilitation of activities in CAD/CAM process through AR will be used for educational purposes and delivered also with AR technology.
Browsers’ Private Mode: Is It What We Were Promised?

Web browsers are one of the most used applications on every computational device in our days. Hence, they play a pivotal role in any forensic investigation and help determine if nefarious or suspicious activity has occurred on that device. Our study investigates the usage of private mode and browsing artefacts within four prevalent web browsers and is focused on analyzing both hard disk and random access memory. Forensic analysis on the target device showed that using private mode matched each of the web browser vendors’ claims, such as that browsing activity, search history, cookies and temporary files that are not saved in the device’s hard disks. However, in volatile memory analysis, a majority of artefacts within the test cases were retrieved. Hence, a malicious actor performing a similar approach could potentially retrieve sensitive information left behind on the device without the user’s consent.
Virtual reality and augmented reality in tourism: Indicative of use by government agencies in Brazilian states

With the cheapening of virtual and augmented reality technologies, sectors such as tourism have started to invest in new ways of promoting destinations, providing an improvement in the user's travel experience. In Brazil, the scenario is no different, private tourism agencies and government agencies responsible for this sphere, invest through their secretariats in the development of products and services allied with these technologies. The objective of the article is to bring a national vision of the scenario, gathering examples of the use of technologies, contributing knowledge through scientific results. The data were collected using a Boolean research formula directed to the official government tourism pages and carried out through the Google search engine, all links that involved the terms "virtual reality" and/or "augmented reality" categorized within the tabs related to tourism on government websites. After analysis of the 84 links recovered, 22 showed adherence to the proposed theme. These were divided by states and regions of Brazil and characterized according to the information published in each of them. The results show a low rate of use of these technologies as some small confusions about the concepts of each one of them. In the whole country, the Midwest and Northeast regions are the ones that presented the largest quantitative and diversified use of augmented reality and virtual reality technologies for government agencies.
Applying the LOT Methodology to a Public Bus Transport Ontology aligned with Transmodel: Challenges and Results

We present an ontology that describes the domain of Public Transport by bus, which is common in cities around the world. This ontology is aligned to Transmodel, a reference model which is available as a UML specification and which was developed to foster interoperability of data about transport systems across Europe. The alignment with this non-ontological resource required the adaptation of the Linked Open Terms (LOT) methodology, which has been used by our team as the methodological framework for the development of many ontologies used for the publication of open city data. The ontology is structured into three main modules: (1) agencies, operators and the lines that they manage, (2) lines, routes, stops and journey patterns, and (3) planned vehicle journeys with their timetables and service calendars. Besides reusing Transmodel concepts, the ontology also reuses common ontology design patterns from GeoSPARQL and the SOSA ontology. As part of the LOT data-driven validation stage, RDF data has been generated taking as input the GTFS feeds (General Transit Feed Specification) provided by the Madrid public bus transport provider (EMT). Mapping rules from structured data sources to RDF were developed using the RDF Mapping Language (RML) to generate RDF data, and queries corresponding to competency questions were tested.
BioGames: a new paradigm and a behavioral biometrics collection tool for research purposes

Purpose The purpose of this paper is to present a new paradigm, named BioGames, for the extraction of behavioral biometrics (BB) conveniently and entertainingly. To apply the BioGames paradigm, the authors developed a BB collection tool for mobile devices named BioGames App. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities and is available on GitHub. Interested researchers and practitioners may use it to create their datasets for research purposes. Design/methodology/approach One major challenge for BB and continuous authentication (CA) research is the lack of actual BB datasets for research purposes. The compilation and refinement of an appropriate set of BB data constitute a challenge and an open problem. The issue is aggravated by the fact that most users are reluctant to participate in long demanding procedures entailed in the collection of research biometric data. As a result, they do not complete the data collection procedure, or they do not complete it correctly. Therefore, the authors propose a new paradigm and introduce a BB collection tool, which they call BioGames, for the extraction of biometric features in a convenient way. The BioGames paradigm proposes a methodology where users play games without participating in an experimental painstaking process. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities. Findings The authors proposed a new paradigm for the collection of BB on mobile devices and created the BioGames application. The BioGames App is an Android application that collects BB data on mobile devices and sends them to a database. The database design allows multiple users to store their sensor data at any time. Thus, there is no concern about data separation and synchronization. BioGames App is General Data Protection Regulation (GDPR) compliant as it collects and processes only anonymous data. Originality/value The BioGames App is a publicly available tool that combines the keystroke dynamics, touch gestures, and motion modalities. In addition, it uses a methodology where users play games without participating in an experimental painstaking process.
Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm

The effectiveness of the Wolf Pack Algorithm (WPA) in high-dimensional discrete optimization problems has been verified in previous studies; however, it usually takes too long to obtain the best solution. This paper proposes the Multi-Population Parallel Wolf Pack Algorithm (MPPWPA), in which the size of the wolf population is reduced by dividing the population into multiple sub-populations that optimize independently at the same time. Using the approximate average division method, the population is divided into multiple equal mass sub-populations whose better individuals constitute an elite sub-population. Through the elite-mass population distribution, those better individuals are optimized twice by the elite sub-population and mass sub-populations, which can accelerate the convergence. In order to maintain the population diversity, population pretreatment is proposed. The sub-populations migrate according to a constant migration probability and the migration of sub-populations are equivalent to the re-division of the confluent population. Finally, the proposed algorithm is carried out in a synchronous parallel system. Through the simulation experiments on the task assignment of the UAV swarm in three scenarios whose dimensions of solution space are 8, 30 and 150, the MPPWPA is verified as being effective in improving the optimization performance.
Freedom of Information and Personal Confidentiality in Spatial COVID-19 Data

Abstract We draw attention to how, in the name of protecting the confidentiality of personal data, national statistical agencies have limited public access to spatial data on COVID-19. We also draw attention to large disparities in the way that access has been limited. In doing so, we distinguish between absolute confidentiality in which the probability of detection is 1, relative confidentiality where this probability is less than 1, and collective confidentiality, which refers to the probability of detection of at least one person. In spatial data, the probability of personal detection is less than 1, and the probability of collective detection varies directly with this probability and COVID-19 morbidity. Statistical agencies have been concerned with relative and collective confidentiality, which they implement using the techniques of truncation, where spatial data are not made public for zones with small populations, and censoring, where exact data are not made public for zones where morbidity is small. Granular spatial data are essential for epidemiological research into COVID-19. We argue that in their reluctance to make these data available to the public, data security officers (DSO) have unreasonably prioritized data protection over freedom of information. We also argue that by attaching importance to relative and collective confidentiality, they have over-indulged in data truncation and censoring. We highlight the need for legislation concerning relative and collective confidentiality, and regulation of DSO practices regarding data truncation and censoring.
Web-based Application Programming Interface (Web APIs): Vacancies in Iranian Public Library Websites

Application Programming Interfaces (APIs) are software tools that help different programs work together. APIs can improve an organization's presence on the Web with tools that integrate various useful, popular programs. This study aimed to identify appropriate web-based APIs used by the most popular public library websites for presentation on Iranian public libraries’ websites. For this purpose, we conducted this study in two stages: In stage one, Web APIs were identified by reviewing the websites of the top public libraries in the world. Then, in stage two, using the obtained results, important Web APIs were selected utilizing experts' opinions (the heuristic method). In stage one, the 30 Web APIs in two categories were identified: 10 public Web APIs and 20 private Web APIs. Then, in stage two, 7 public APIs and 17 private Web APIs for these websites were selected, based on expert analysis. The results of this study can be used to improve the design of public library websites and enhance the communication of such websites' presence on the Web.
Multi‐task tracking and classification with an adaptive radar

This study investigates the use of adaptive radar waveforms in performing a multi-task operation involving the tracking and classification of a single target. The work applies a previously reported hierarchical fully adaptive radar framework and considers a situation in which the target tracking function is performed using a transmit waveform having adaptive pulse repetition frequency, pulse width, bandwidth, and coherent processing interval parameters, while the classification function can be performed using only a fixed radar waveform. Two alternate strategies for selecting the next waveform are then described. The first strategy is focussed on maintaining fixed measurement performance goals in terms of target range and velocity and classification entropy, while the second approach varies these performance goals depending on the target state. Both approaches are examined for simulated datasets at W-band representing a moving human target, as well as for laboratory measurements of this scenario. The results for both simulation and experiment show the benefits of including adaptive waveforms in performing multiple radar tasks.
Automatic Title Generation for Learning Resources and Pathways with Pre-trained Transformer Models

To create curiosity and interest for a topic in online learning is a challenging task. A good preview that outlines the contents of a learning pathway could help learners know the topic and get interested in it. Towards this end, we propose a hierarchical title generation approach to generate semantically relevant titles for the learning resources in a learning pathway and a title for the pathway itself. Our approach to Automatic Title Generation for a given text is based on pre-trained Transformer Language Model GPT-2. A pool of candidate titles are generated and an appropriate title is selected among them which is then refined or de-noised to get the final title. The model is trained on research paper abstracts from arXiv and evaluated on three different test sets. We show that it generates semantically and syntactically relevant titles as reflected in ROUGE, BLEU scores and human evaluations. We propose an optional abstractive Summarizer module based on pre-trained Transformer model T5 to shorten medium length documents. This module is also trained and evaluated on research papers from arXiv dataset. Finally, we show that the proposed model of hierarchical title generation for learning pathways has promising results.