Reviewerly/exHarmony huggingface dataset:
Expanding Non-Patient COVID-19 Data: Towards the FAIRification of Migrants’ Data in Tunisia, Libya and Niger

Abstract This article describes the FAIRification process (which involves making data Findable, Accessible, Interoperable and Reusable—or FAIR—for both machines and humans) for data related to the impact of COVID-19 on migrants, refugees and asylum seekers in Tunisia, Libya and Niger, according to the scheme adopted by GO FAIR. This process was divided into three phases: pre-FAIRification, FAIRification and post-FAIRification. Each phase consisted of seven steps. In the first phase, 118 in-depth interviews and 565 press articles and research reports were collected by students and researchers at the University of Sousse in Tunisia and researchers in Niger. These interviews, articles and reports constitute the dataset for this research. In the second phase, the data were sorted and converted into a machine actionable format and published on a FAIR Data Point hosted at the University of Sousse. In the third phase, an assessment of the implementation of the FAIR Guidelines was undertaken. Certain barriers and challenges were faced in this process and solutions were found. For FAIR data curation, certain changes need to be made to the technical process. People need to be convinced to make these changes and that the implementation of FAIR will generate a long-term return on investment. Although the implementation of FAIR Guidelines is not straightforward, making our resources FAIR is essential to achieving better science together.
Curriculum Development for FAIR Data Stewardship

Abstract The FAIR Guidelines attempts to make digital data Findable, Accessible, Interoperable, and Reusable (FAIR). To prepare FAIR data, a new data science discipline known as data stewardship is emerging and, as the FAIR Guidelines gain more acceptance, an increase in the demand for data stewards is expected. Consequently, there is a need to develop curricula to foster professional skills in data stewardship through effective knowledge communication. There have been a number of initiatives aimed at bridging the gap in FAIR data management training through both formal and informal programmes. This article describes the experience of developing a digital initiative for FAIR data management training under the Digital Innovations and Skills Hub (DISH) project. The FAIR Data Management course offers 6 short on-demand certificate modules over 12 weeks. The modules are divided into two sets: FAIR data and data science. The core subjects cover elementary topics in data science, regulatory frameworks, FAIR data management, intermediate to advanced topics in FAIR Data Point installation, and FAIR data in the management of healthcare and semantic data. Each week, participants are required to devote 7–8 hours of self-study to the modules, based on the resources provided. Once they have satisfied all requirements, students are certified as FAIR data scientists and qualified to serve as both FAIR data stewards and analysts. It is expected that in-depth and focused curricula development with diverse participants will build a core of FAIR data scientists for Data Competence Centres and encourage the rapid adoption of the FAIR Guidelines for research and development.
Implementation of FAIR Guidelines in Selected Non-Western Geographies

Abstract This study provides an analysis of the implementation of FAIR Guidelines in selected non-Western geographies. The analysis was based on a systematic literature review to determine if the findability, accessibility, interoperability, and reusability of data is seen as an issue, if the adoption of the FAIR Guidelines is seen as a solution, and if the climate is conducive to the implementation of the FAIR Guidelines. The results show that the FAIR Guidelines have been discussed in most of the countries studied, which have identified data sharing and the reusability of research data as an issue (e.g., Kazakhstan, Russia, countries in the Middle East), and partially introduced in others (e.g., Indonesia). In Indonesia, a FAIR equivalent system has been introduced, although certain functions need to be added for data to be entirely FAIR. In Japan, both FAIR equivalent systems and FAIR-based systems have been adopted and created, and the acceptance of FAIR-based systems is recommended by the Government of Japan. In a number of African countries, the FAIR Guidelines are in the process of being implemented and the implementation of FAIR is well supported. In conclusion, a window of opportunity for implementing the FAIR Guidelines is open in most of the countries studied, however, more awareness needs to be raised about the benefits of FAIR in Russia and Kazakhstan to place it firmly on the policy agenda.
Comparing Student and Recruiter Evaluations of Computer Science Resumes

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This study identifies which entry-level computer science (CS) resume items are most important and compares the ratings of student and recruiter participants to investigate the accuracy of student beliefs. To the authors’ knowledge, this study is the first to analyze the extent to which CS students understand the resume screening process. The results of this have consequences for students in their own resume development. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Although prior research studies the importance of different resume items generally, little is known about resumes for CS majors, which may contain distinctive sections. Less still is known about whether students understand the resume screening process. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research Questions:</i> Which items on entry-level CS resumes most directly influence screening decisions? What gaps exist between CS students’ and recruiters’ perceptions of resume items? Does the disparity in expertise between students and recruiters contribute to differences in resume screening? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> 197 recruiters and 73 CS undergraduates screened randomized CS resumes. Data were analyzed using ordinary least-squares regression with interactions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> Students were more likely than recruiters to move resumes to the next level and spent about 7 s less than recruiters when screening resumes. Though students correctly assessed the value of most resume items, they significantly overestimated the value of prior work experience such as internships.
Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

Big data and cloud computing are two advanced technologies that have overcome many computing and analytical challenges in recent years.With the rise in the applications of these technologies, the necessity of efficiency and optimization in the utilization of related resources has made sense.The procedure of locating virtual machines (VM) in physical machines (PM) affects the performance, speed, and costs of cloud computing services.VM placement in cloud computing is an NP-hard problem.Indeed, the problem is more complicated in big data tasks due to the need for transferring high volumes of traffic between VMs.This paper proposes a new approach for VM placement in a multi data center (DC) cloud environment.The aware genetic algorithm first fit (AGAFF) is a context-aware algorithm that distinguishes big data tasks with an input tag and uses a structure to minimize the traffic between MapReduce nodes.This multi-objective algorithm is based on the genetic algorithm, which is incorporated with the first fit methodology.The algorithm minimizes energy usage by minimizing the number of used servers, intra-DC traffic of big data tasks, and VMs' live migration while maximizing relevant usage of CPU and RAM in every server.Furthermore, it improves job execution time, especially in big data processing, and reduces service level agreement (SLA) violations.A comparison between the results of AGAFF and four other algorithms shows by about 61% energy consumption reduction on average on different scales and approves a decrease in the number of needed PMs, intra-DC traffic of big data processing, and the number of live migrations.
P$^{2}$-GAN: Efficient Stroke Style Transfer Using Single Style Image

Style transfer is a useful image synthesis technique that can re-render given image into another artistic style while preserving its content information. Generative Adversarial Network (GAN) is a widely adopted framework toward this task for its better representation ability on local style patterns than the traditional Gram-matrix based methods. However, most previous methods rely on sufficient amount of pre-collected style images to train the model. In this paper, a novel Patch Permutation GAN (P <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -GAN) network that can efficiently learn the stroke style from a single style image is proposed. We use patch permutation to generate multiple training samples from the given style image. A patch discriminator that can simultaneously process patch-wise images and natural images seamlessly is designed. We also propose a local texture descriptor based criterion to quantitatively evaluate the style transfer quality. Experimental results showed that our method can produce finer quality re-renderings from single style image with improved computational efficiency compared with many state-of-the-arts methods.
Towards Evidence-based Argumentation Graph for Clinical Decision Support

Clinical decision-making is closely related with the activity of argumentation among alternative options. In recent years, theories and languages have been developed for argumentation and evidence-based decision support. However, a systematic study of argument representation using evidence in the medicine domain is missing. In this paper, an Evidence-based Argumentation Graph is proposed. A Clinical Argumentation scheme and a Patient Preference Argumentation scheme guide their construction. Arguments can be represented using clinical and patient preference evidence and semantically integrated in the graph. Clinical decision support is delivered to clinicians and patients together. The method is demonstrated using a case study of decision support for patients suspected with breast cancer.
Ensemble Framework for Unsupervised Cervical Cell Segmentation

In medical image segmentation, preparing ground truths (or masks) is not trivial as it requires expert clinicians to manually label regions-of-interest. Cervical cytology image segmentation is no exception. In this paper, we propose an unsupervised segmentation framework for cervical cell and whole slide segmentation uses an ensemble of three clustering algorithms namely, K-means, K-means++ and Mean Shift clustering. The final cluster centers obtained from these algorithms are used to initialize cluster points for Fuzzy C-means clustering algorithm. The proposed method is evaluated on multiple standard datasets: HErlev Pap Smear dataset and SIPaKMeD Pap Smear dataset. We also evaluated on a whole slide image dataset (source: CMATER-JU laboratory) and our results are promising and comparable. Overall, our results on multiple benchmark datasets justify the viability of the proposed framework.
Stable Contrastive Learning for Self-Supervised Sentence Embeddings With Pseudo-Siamese Mutual Learning

Learning semantic sentence embeddings is beneficial to a variety of natural language processing tasks. Recently, methods using the contrastive learning framework to fine-tune pre-trained language models have been proposed and have achieved significant performance on sentence embeddings. However, sentence embeddings are easy to “overfit” to the contrastive learning goal. With the training of contrastive learning, the gap between contrastive learning and test tasks leads to unstable even declining performance on test tasks. For this reason, existing methods rely on the labeled development set to frequently evaluate the performance on test tasks and get the best checkpoints. In such a way, models are limited when the labeled data is unavailable or extremely scarce. To address this problem, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> seudo- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> iamese network <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> utual <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> earning (PSML) for self-supervised sentence embeddings to reduce the gap between contrastive learning and test tasks. Consisting of the main encoder and the auxiliary encoder, PSML utilizes mutual learning as the basic framework. Between the two encoders, two mutual learning losses are constructed to share learning signals. The proposed model framework and losses of PSML help the model be optimized more stably and generalize better to test tasks, such as semantic textual similarity. Extensive experiments on seven public semantic textual similarity datasets show that PSML performs better than previous unsupervised contrastive methods for sentence embeddings. Besides, PSML also gives a stable performance curve on test tasks with training and is able to get the comparative performance without frequent evaluation on the labeled development set.
Investigating the Effectiveness of Color Coding in Multimodal Medical Imaging

In medical imaging, images represent the quantification of the interaction between electromagnetic waves and our body and are represented in grey-scale. In addition, medical imaging often produces multimodal images. However, the analysis and interpretation of these images mostly occur in sequence or, as in the case of automatic tools, they are simply concatenated as independent sources of information. In both cases, color perception and color contrast are not exploited. Color perception and color contrast play a crucial role in human vision to recognize objects effectively and efficiently, and this can in principle extend to automatic systems. In this paper we show how color coding, particularly using color opponent models, can become an effective tool for preliminary color-based segmentation. Tests have been conducted on multimodal Magnetic Resonance Imaging (MRI) of the brain collected in a public database and the results obtained show the importance of color coding in medical imaging analysis.
Methodological Aspects of the Application of Model Theory in Knowledge Engineering and Artificial Intelligence

The paper deals with methodological aspects and methodological problems of applying the Model Theory for solving problems of extraction, formal representation and processing of knowledge, formalization of reasoning about events and situations in various subject domains. Methodological problems associated with the need to use transformations of models of different signatures, such as isomorphic embeddings, submodels and homomorphisms, etc., are considered in the modeling of subject domains. The model-theoretical formalization of subject domain ontologies is considered. Formal glossaries are used as a tool for describing the meaning of concepts. The role of explicit and implicit definitions of key concepts of the subject domain is investigated. Model-theoretical methods are proposed for translating a language containing multi-ary predicates into a language containing only unary and binary predicates. These methods are based on the introduction of special situation-constants. Methodological problems associated with the need to formalize incomplete knowledge about situations and cases of subject domains are considered. For the formal representation of situations, methods of the theory of partial models are studied, submodels and ontological homomorphisms of partial models are considered. Model-theoretical methods for formalizing and automating reasoning based on the use of situation trees are proposed. The model-theoretical methods of knowledge representation and processing considered in the article are used to develop semantic artificial intelligence. These methods are used to solve the problem of explainable artificial intelligence.
Fusing industry and academia at GitHub (experience report)

GitHub hosts hundreds of millions of code repositories written in hundreds of different programming languages. In addition to its hosting services, GitHub provides data and insights into code, such as vulnerability analysis and code navigation, with which users can improve and understand their software development process. GitHub has built Semantic, a program analysis tool capable of parsing and extracting detailed information from source code. The development of Semantic has relied extensively on the functional programming literature; this paper describes how connections to academic research inspired and informed the development of an industrial-scale program analysis toolkit.
Monadic compiler calculation (functional pearl)

Bahr and Hutton recently developed a new approach to calculating correct compilers directly from specifications of their correctness. However, the methodology only considers converging behaviour of the source language, which means that the compiler could potentially produce arbitrary, erroneous code for source programs that diverge. In this article, we show how the methodology can naturally be extended to support the calculation of compilers that address both convergent and divergent behaviour simultaneously , without the need for separate reasoning for each aspect. Our approach is based on the use of the partiality monad to make divergence explicit, together with the use of strong bisimilarity to support equational-style calculations, but also generalises to other forms of effect by changing the underlying monad.
Developing A Conceptual Passive Contact Tracing System for Commercial Buildings Using WiFi Indoor Positioning

Contact tracing is one of the critical tools for fighting against pandemic disease outbreaks, such as the fast-growing SARS-CoV-2 virus and its different variants. At present, automated contact tracing systems face two main challenges: (1) requiring application installation on smart devices and (2) protecting the users’ privacy. This study introduces a conceptual passive contact tracing system using indoor WiFi positioning to address these challenges and investigate the role of such a system in commercial buildings. In this regard, this study uses a simulated small-office layout in a case study to demonstrate the applicability of the proposed system. The special use of the proposed contact tracing system could be academic facilities and office buildings, where (1) the WiFi infrastructure already exists and therefore implementing such a system could be cost-effective, and (2) the same users use the facility regularly, enabling the system to notify the users upon a confirmed case once they are back in the building and connected to the WiFi system. Such technology can not only enhance the current automated contact tracing system in commercial buildings by illuminating the need to use smartphone applications while protecting users’ privacy, but could also reduce the risk of infection in indoor environments. The developed system can benefit facility managers, business owners, policy makers, and authorities in assisting to find occupants’ high-risk contacts and control the spread of SARS-CoV-2 or similar infectious diseases in commercial buildings, particularly university campuses and office buildings.
Creating Concrete Visions with Augmented Reality

Overview:Augmented reality (AR) is an integrative digital transformation with impacts extending beyond design professionals and gamers. We examine five cases of AR use in the built environment in architecture, construction, and planning. The cases used the same focal AR platform, which runs on mobile devices, AR headsets, and personal computers, bringing AR to a broad audience. The applications follow a sociotechnical approach; the organizations manage talent, technology, and technique, in concert. AR allowed greater participation in the designs and presentations with benefits across understanding, performance, and commitment in these settings. We describe the implementations with an accessible framing of sociotechnical systems and introduce a trope: "See in 3D—Think in 5T." Companies can use AR to align their talent, technology, and technique to their target and the times.
Analysis of Hyper-Parameters for AlphaZero-Like Deep Reinforcement Learning

The landmark achievements of AlphaGo Zero have created great research interest into self-play in reinforcement learning. In self-play, Monte Carlo Tree Search (MCTS) is used to train a deep neural network, which is then used itself in tree searches. The training is governed by many hyper-parameters. There has been surprisingly little research on design choices for hyper-parameter values and loss functions, presumably because of the prohibitive computational cost to explore the parameter space. In this paper, we investigate 12 hyper-parameters in an AlphaZero-like self-play algorithm and evaluate how these parameters contribute to training. Through multi-objective analysis, we identify four important hyper-parameters to further assess. To start, we find surprising results where too much training can sometimes lead to lower performance. Our main result is that the number of self-play iterations subsumes MCTS-search simulations, game episodes and training epochs. As a consequence of our experiments, we provide recommendations on setting hyper-parameter values in self-play. The outer loop of self-play iterations should be emphasized, in favor of the inner loop. This means hyper-parameters for the inner loop, should be set to lower values. A secondary result of our experiments concerns the choice of optimization goals, for which we also provide recommendations.