Reviewerly/exHarmony huggingface dataset:
Debating E-voting throughout Europe: constitutional structures, parties' concepts and Europeans' perceptions

Techno-optimists with a more cosmopolitan focus agree that E-voting lies at the heart of implementing e-government and digitalization into democratic structures. The example of the “e-state” Estonia proves the relevance: E-voting has been in (legal) practice since 2005, and research can take much empirical evidence from this laboratory for digital innovation. The fact that Estonia is an exception to the rule within the European Union (EU) member states explains the comparative approach to the (possible) legal framework for eparticipation. With focusing on liberal democracies' constitutional predefinitions, voting procedures in the virtual age have not been compared yet. However, we have yet to learn much about the extent to which E-voting exists in European constitutions, even after one generation of intense debate about its possible implementation. Perceptions of E-voting matter because of the omnipresent digital transformation and discussions about how democracies (could) digitalize. E-voting represents a bottom-up part of top-down e-government and, through this, digital transformation. This research explores whether party policies, legal frameworks, and citizens' perceptions resemble E-voting on the national and European levels. To explore this question, several mixed-methods approaches are used. The question of “legalistic opportunity structures” is approached by relying on legal frameworks of European member states, parties' policies derived from their manifestos, and survey data from three Eurobarometer waves. Using a dictionary approach, the research design analyses the constitutions, electoral laws and manifestos of parties running for the European elections, combined with a classic analysis of surveys. Therefore, these sources are analyzed using several mixed methods approaches. The results have broader implications that we need to study in more detail what the digital transformation and the constitutionalization of electronic decision-making entail to develop a digital democracy and link it to a public sphere throughout Europe. Ultimately, it is analyzed whether the EU will push its member states to E-voting and implement E-voting for European elections. This would question the normative basing of democracy and how responsivity is brought into place.
Application of Multicriteria Methods for Improvement of Information Security Metrics

Metrics are a set of numbers that are used to obtain information about the operation of a process or system. In our case, metrics are used to assess the level of information security of information and communication infrastructure facilities. Metrics in the field of information security are used to quantify the possibility of damage due to unauthorized hacking of an information system, which make it possible to assess the cyber sustainability of the system. The purpose of the paper is to improve information security metrics using multicriteria decision–making methods (MCDM). This is achieved by proposing aggregated information security metrics and evaluating the effectiveness of their application. Classical information security metrics consist of one size or one variable. We obtained the total value by adding at least two different metrics and evaluating the weighting factors that determine their importance. This is what we call aggregated or multicriteria metrics of information security. Consequently, MCDM methods are applied to compile aggregated metrics of information security. These are derived from expert judgement and are proposed for the three management domains of the ISO/IEC 27001 information security standard. The proposed methods for improving cyber sustainability metrics are also relevant to information security metrics. Using AHP, WASPAS and Fuzzy TOPSIS methods to solve the problem, the weights of classical metrics are calculated and three aggregated metrics are proposed. As a result, to confirm the fulfilment of the task of improving information security metrics, a verification experiment is conducted, during which aggregated and classical information security metrics are compared. The experiment shows that the use of aggregated metrics can be a more convenient and faster process and higher intelligibility is also achieved.
InP-based CV-QKD PIC Transmitter

An InP-based photonic integrated circuit (PIC) transmitter for pulsed Gaussian-modulated coherent-state (GMCS) CV-QKD protocol is presented and characterized. Results show potential asymptotic secret key rates of 0.4 Mbps at 11 km, and up to 2.3 Mbps in back-to-back configuration.
The Effectiveness of Using Kinect-Based Dyslexia Therapy in Improving Reading Ability in Dyslexic Children: A Linguistic Study

Dyslexia is a learning disorder characterized by difficult in reading, writing, or spelling. People especially children with dyslexia have difficult to identify spoken words and converting them into letters or sentences. The aims of this study are 1) to find out the early symptoms of dyslexia in children, 2) to reveal the effectiveness of using video game media (kinect-based dyslexia therapy media) in improving reading skills in dyslexic children. This research used quantitative method. This research is an experimental longitudinal because there is an intensive observation of the subject within a certain period of time. The result of this study is to reduce the dyslexia children as much as possible to read by using the intervention “Kinect-Based Dyslexia Therapy” (LexiPal). Strong evidence of the success of the LexiPal intervention can be seen through the experimental method (calculated the difference in the position of the treatment group and the control group at the end of the experiment). Calculations are carried out through simple statistical calculations.
Towards Pragmatic Semantic Image Synthesis for Urban Scenes

The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount of paired data, incurring extra costs. In this work, we present a new task: given a dataset with synthetic images and labels and a dataset with unlabeled real images, our goal is to learn a model that can generate images with the content of the input mask and the appearance of real images. This new task reframes the well-known unsupervised SIS task in a more practical setting, where we leverage cheaply available synthetic data from a driving simulator to learn how to generate photorealistic images of urban scenes. This stands in contrast to previous works, which assume that labels and images come from the same domain but are unpaired during training. We find that previous unsupervised works underperform on this task, as they do not handle distribution shifts between two different domains. To bypass these problems, we propose a novel framework with two main contributions. First, we leverage the synthetic image as a guide to the content of the generated image by penalizing the difference between their high-level features on a patch level. Second, in contrast to previous works which employ one discriminator that overfits the target domain semantic distribution, we employ a discriminator for the whole image and multiscale discriminators on the image patches. Extensive comparisons on the benchmarks GTA-V $\rightarrow$ Cityscapes and GTA-V $\rightarrow$ Mapillary show the superior performance of the proposed model against state-of-the-art on this task.
Patient Electronic Health Record as Temporal Graphs for Health Monitoring

Machine learning methods are becoming increasingly popular to anticipate critical risks in patients under surveillance reducing the burden on caregivers. In this paper, we propose an original modeling that benefits of recent developments in Graph Convolutional Networks: a patient's journey is seen as a graph, where each node is an event and temporal proximities are represented by weighted directed edges. We evaluated this model to predict death at 24 hours on a real dataset and successfully compared our results with the state of the art.
Serverless and IaC

Abstract: The objective of this research paper is to investigate the potential advantages of integrating Terraform and Serverless Computing to construct a scalable and efficient cloud infrastructure. Terraform, an infrastructure-as-code open-source tool, and Serverless Computing, a new paradigm that enables developers to run code without worrying about the underlying infrastructure, are briefly described along with their benefits. The paper then explores how these two can be combined to build a dynamic and robust infrastructure while also addressing the difficulties that arise when constructing a Serverless Computing infrastructure with Terraform, proposing solutions to overcome them. Finally, the study concludes that integrating Terraform and Serverless Computing can help organizations create an efficient, adaptable, and scalable infrastructure while decreasing expenses and enhancing developer productivity.
The influence of humble leadership on employees' active self-service performance -- the mediating role of affective commitment and the moderating role of job autonomy

With the continuous development of social economy and the increasingly fierce competition among enterprises, how to improve employees' active self-service performance has attracted more and more scholars' attention. Based on the active motivation model, starting from the affective commitment and job autonomy of employees, this paper analyzes 328 valid sample data, and discusses the role and adjustment mechanism of humble leaders on employees' active self-service performance. The results show that humble leadership has a significant positive effect on employees' active self-service performance, and affective commitment plays a completely mediating role. In addition, job autonomy is an important boundary condition that affects this mechanism. When the level of job autonomy is high, the impact of humble leadership on employees' active self-service performance through affective commitment will increase. On the contrary, when the level of job autonomy is low, the interaction between humble leadership and job autonomy will decrease, and the degree of positive effect will be negatively affected.
Changing Face of Indian Political Cartoons from 2010–2020

Political cartoons are a reflection of the politics in the country and democracy as a whole. As time changed, media and politics changed, too. Therefore, it is important to understand how political cartoons are changing, especially when they are known to play a critical role in democracy. Comparison and analysis of political cartoons over time could bring about reasonable findings. The present research focused on mending this gap in the existing literature and analyzed the changing face of Indian political cartoons over the past decade. The present study was conducted on Editors of different editorials (English and Hindi) based in the Delhi-NCR region, India. Primary data were used in the study, wherein the interview method was adopted to collect the data in the form of interview transcripts. Herein, qualitative analysis was conducted wherein the interview method was adopted. The study’s findings threw light on how the face of political cartoons has changed over the past decade. The research findings could be used by practicing political cartoonists to update themselves. Further, the newspaper could also use this study to analyze how to keep the dying art of political cartooning alive. The research conducted on political cartooning is very limited, and the focus on changes in political cartooning is almost zero. Thus, the present research is one of its kind.
Big optimization with genetic algorithms: Hadoop, Spark, and MPI

Solving problems of high dimensionality (and complexity) usually needs the intense use of technologies, like parallelism, advanced computers and new types of algorithms. MapReduce (MR) is a computing paradigm long time existing in computer science that has been proposed in the last years for dealing with big data applications, though it could also be used for many other tasks. In this article, we address big optimization: the solution to large instances of combinatorial optimization problems by using MR as the paradigm to design solvers that allow transparent runs on a varied number of computers that collaborate to find the problem solution. We study and analyze the MR technology, focusing on Hadoop, Spark, and MPI as the middleware platforms to develop genetic algorithms (GAs). From this, MRGA solvers arise using a different programming paradigm from the usual imperative transformational programming. Our objective is to confirm the expected benefits of these systems, namely file, memory, and communication management, over the resulting algorithms. We analyze our MRGA solvers from relevant points of view like scalability, speedup, and communication vs. computation time in big optimization. The results for high-dimensional datasets show that the MRGA over Hadoop outperforms the implementations in Spark and MPI frameworks. For the smallest datasets, the execution of MRGA on MPI is always faster than the executions of the remaining MRGAs. Finally, the MRGA over Spark presents the lowest communication times. Numerical and time insights are given in our work, so as to ease future comparisons of new algorithms over these three popular technologies.
Common Problems With the Usage of F-Measure and Accuracy Metrics in Medical Research

Problem Binary classifiers are widely used in medical research, especially for diagnoses. They are usually evaluated via performance metrics computed based on confusion matrices. Accuracy and F-measure are among the most frequently used performance metrics, but they make implicit assumptions and do not take into account important characteristics of classifiers. As a consequence, evaluations based on Accuracy or F-measure may turn out to be incorrect, unreliable, and inadequate for the specific application context. The usage of Accuracy and F-measure is particularly critical in the medical domain, where selecting a sub-optimal classifier may lead to incorrect diagnoses, with potentially serious or even fatal consequences. Aim We investigated whether the improper or naive usage of Accuracy and F-measure can lead to partial or incorrect evaluations. If this is the case, we need a procedure to reinterpret the conclusions reported in research articles, whenever possible. Method After discussing a few important properties of Accuracy and F-measure, we examine a set of representative research articles, to assess their conclusions, and illustrate a procedure to reinterpret those conclusions. Results It appears that the examined research articles yield conclusions that are largely affected by the used performance metrics, which in some cases lead to very misleading conclusions. The application of the proposed procedure allows the retrieval of confusion matrices and the derivation of reliable indications of classifiers’ performances. Conclusion F-measure and Accuracy should be used with care, being aware of their characteristics and limits. We recommend that future evaluations of binary classifiers be provided with the complete confusion matrices, so that users can formulate evaluations based on specific contexts and priorities.
Northern Gumuz

Abstract This chapter describes the Nilo-Saharan language, Northern Gumuz (NoG), that forms part of the broader Gumuz dialect cluster spoken across northwestern Ethiopia and southeastern Republic of Sudan. The speakers of NoG live side-by-side among ethnic groups who speak other mother tongue languages such as Amharic, Awngi, Shinasha and Oromo. Debates arise regarding the inclusion of the Gumuz languages (and the broader B’aga language family) in the Nilo-Saharan phylum. While the basic phonology and syntax of the language are described here, some unusual characteristics of the language include complex verb stems comprised of a verb root and (historical) noun root. Some of these historical noun roots (that form complex verb stems) have grammaticalized as verbal classifiers, making Gumuz the only language(s) in Africa documented with such a classifier system.
Multicultural Education in the Forming of Social Character in the Digitalization Era

The purpose of the study was to find out the implementation of multicultural education studies in the digital era and the implications of multicultural education on the formation of social character in the digitalization era, with the rapid development of technology today, it certainly has an impact on the condition of education, including the study of multicultural education. The method used in this article is a qualitative method with a literature review. Data collection techniques are carried out with a conceptual approach. The data collection technique in this study was carried out with documentation techniques. This technique is used to track documents, data, and information in books, magazines, scientific journals, articles, and so on that are relevant to this research. The author uses content analysis (content analysis) to analyze the data. The results of the study show that the condition of the rapid progress of information and communication technology has a negative impact. Therefore multicultural education, in this case, seeks to provide understanding to the community, especially students, to know about multicultural values, namely the attitude of accepting, appreciating, and respecting all forms of difference. To avoid unwanted conditions such as radicalism, racism, discrimination, and so on.
Gawwada

Abstract This chapter focuses on the dialect spoken in the town of Gawwada and the neighbouring villages—Gawwada. It stresses that it is a member of the so-called Dullay variety spoken in the town of Gawwada and the neighbouring villages in southwest Ethiopia. Dullay is a direct offspring of East Cushitic, although an alternative classification grouping Dullay and Yaaku together has been proposed and further substantiated. I propose to call the groups made up of Dullay and Yaaku Peripheral East Cushitic. Dullay speakers have no overall term for themselves and the Dullay dialects are not endangered. This chapter then shifts to discuss the phonology of Gawwada, starting with the inventory of phonemes. It studies the process of affixation and reduplication in Gawwada, and notes that suffixation is the most common morphological mechanism. The chapter concludes by discussing adjectives, numerals, ideophones, and negation.
Education and Economic Growth: The Causal Relationship in Sierra Leone

The objective of the paper is to investigate the value-addition that expenditure on education has on economic growth in the Sierra Leone. The vector autoregression methodology is applied to annual data spanning 2000 â€“ 2021, taking the time series properties of variables into consideration. A shock to government investment in education shows a positive response of growth but not immediately, which confirm that investment in education is conducive to long-term growth in Sierra Leone. Given government budget constraint, especially under a highly constrained tax revenue mobilization, efforts in building Public-Private-Partnership (PPP) as a way of facilitating sustainable long-term economic growth is imperative while increased donor support for Government investment in education is important for sustained inclusive growth in Sierra Leone.
Modified centroid triplet loss for person re-identification

Abstract Person Re-identification (ReID) is the process of matching target individuals to their images within different images or videos captured from a variety of angles or cameras. This is a critical task for surveillance applications, in particular, these applications that operate in large environments such as malls and airports. Recent studies use data-driven approaches to tackle this problem. This work continues on this path by presenting a modification of a previously defined loss, the centroid triplet loss ( CTL). The proposed loss, modified centroid triplet loss (MCTL), emphasizes more on the interclass distance. It is divided into two parts, one penalizes for interclass distance and second penalizes for intraclass distance. Mean Average Precision (mAP) was adopted to validate our approach, two datasets are also used for validation; Market-1501 and DukeMTMC. The results were calculated for first rank of identification and mAP. For dataset Market-1501 dataset, the results were $$98.4\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>98.4</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> rank1, $$98.63\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>98.63</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> mAP, and $$96.8\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>96.8</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> rank1, $$97.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>97.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> mAP on DukeMTMC dataset, the results outweighed those of existing studies in the domain.
Seaweed plastics hit the table

The seaweed plastics start-up Loliware has launched a grade of its resins for injection molding. The materials have been fashioned into what the company calls the world’s first seaweed-based utensil set. Loliware says its materials can biodegrade as fast as banana peels.
EFFECTIVENESS OF ONLINE LEARNING IN HOSPITALITY AND TOURISM MANAGEMENT STUDY PROGRAM AT RUNATA BUSINESS COLLEGE

This research aims to find out the effectiveness of online learning in the Hospitality and Tourism Management Study Program at Runata Business College. This research is descriptive analysis study by using a quantitative approach. The data were collected by using questionnaires, interviews, observation, and documentation methods. The number of samples used in this study was 30 students who were determined based on the census sample method. The validity test in this study used Alpha Cronbach, while data analysis used simple linear regression. The results of this study found that effectiveness had a positive and significant effect on online learning, with a value of 0.029&lt;0.050 which identifying that H0 was rejected and H1 was accepted.
A Model of Qualitative Factors in Forensic-Ready Software Systems

Abstract Forensic-ready software systems enhance the security posture by designing the systems prepared for potential investigation of incidents. Yet, the principal obstacle is defining their exact requirements, i.e., what they should implement. Such a requirement needs to be on-point and verifiable. However, what exactly comprises a forensic readiness requirement is not fully understood due to distinct fields of expertise in software engineering and digital forensics. This paper describes a forensic readiness qualitative factor reference model that enables the formulation of specific requirements for forensic-ready software systems. It organises the qualitative properties of forensic readiness into a taxonomy, which can then be used to formulate a verifiable requirement targeted at a specific quality. The model is then utilised in an automated valet parking service to define requirements addressing found inadequacies regarding a potential incident investigation.