Reviewerly/exHarmony huggingface dataset:
Towards open research: practices, experiences, barriers and opportunities (“Nicolae Testemitanu” USMPh case study)

We are in the midst of progress and change regarding open science and open access to research data. There is a particular emphasis on good data practices, including data management and sharing plans. Open access to research data can help speed up the pace of discovery and provide more value and credibility to publicly funded research. The purpose of the study is to identify the attitudes and perceptions of the university medical community regarding the opening of data and to establish the role of the Scientific Medical Library in the management of research data. Applied research methods: sociological – the survey was based on the online questionnaire, which included 24 closed structured questions (with a single answer and with multiple answers), open (gives the opportunity to the respondent to answer as he wants, without any constraint), mixed, of opinion, as well as of scale, the non-random sampling, on a voluntary basis, research data analysis, and synthesis. The hypotheses established at the beginning of the research were confirmed, most respondents lack sufficient knowledge and skills about open research data. The study demonstrates an acknowledgment of the benefits of open data. The summative evaluation of the results allowed the identification of the essential factors that motivate the research activity: increasing visibility in the international scientific community, prestige, and career. At the same time, the impediments to the opening of research data were identified: lack of culture (knowledge) regarding data sharing and respectively researchers are not ready to share research data, the lack of a policy on research data management, problems related to copyright and intellectual property, data sensitivity, fear of misuse of research data, do not know where to publish such data, lack of knowledge about data and metadata standards, etc. Opening access to research results, data sharing, increasing research transparency, and adopting the Open Science model would accelerate the development rate of the Republic of Moldova. The opening of research data will improve the institutions position in international rankings, enhancing academic prestige and bringing additional financing. The Scientific Medical Library will organize information sessions for the university medical community, will promote the open data principle, will raise awareness among the university medical community regarding the benefits of open data, as well as participate in the development of institutional policy and the research data management plan at “Nicolae Testemitanu” State University of Medicine and Pharmacy, will develop a concept of open research data repository, etc.
Librarians training on Open Science

Libraries are the basic facilitators of Open Science (OS). Continuous training of librarians on OS is one of the key requirements to be able to take advantage of Open Access (OA) opportunities and be actively involved in promoting the benefits of OA for society. The article mentions the contribution of the LIBER Digital Skills Working Group, which aims to spread a more open culture and position libraries as key partners in developing skills for the complex and forward-looking concept of OS. As an active member of the research group, the essential tasks of librarians on OS are indicated. An analysis of the situation regarding the training of specialized personnel in the libraries of the Republic of Moldova (educational practices, content and subject of training, training needs) is undertaken. Ways of training or developing skills in OS are identified, such as the impact of training and the development of skills by librarians on OS.
BioShare: An Open Framework for Trusted Biometric Authentication under User Control

Generally, biometric authentication is conducted either by mobile terminals in local-processing mode or by public servers in centralized-processing mode. In the former mode, each user has full control of his/her biometric data, but the authentication service is restricted to local mobile apps. In the latter mode, the authentication service can be opened up to network applications, but the owners have no control of their private data. It has become a difficult problem for biometric applications to provide open and trusted authentication services under user control. Existing approaches address these concerns in ad-hoc ways. In this work, we propose BioShare, a framework that provides trusted biometric authentication services to network applications while giving users full control of their biometric data. Our framework is designed around three key principles: each user has full control of his/her biometric data; biometric data is stored and processed in trusted environments to prevent privacy leaks; and the open biometric-authentication service is efficiently provided to network applications. We describe our current design and sample implementation, and illustrate how it provides an open face-recognition service with standard interfaces, combines terminal trusted environments with server enclaves, and enables each user to control his/her biometric data efficiently. Finally, we analyze the security of the framework and measure the performance of the implementation.
UniGPS: A Unified Programming Framework for Distributed Graph Processing

The industry and academia have proposed many distributed graph processing systems. However, the existing systems are not friendly enough for users like data analysts and algorithm engineers. On the one hand, the programing models and interfaces differ a lot in the existing systems, leading to high learning costs and program migration costs. On the other hand, these graph processing systems are tightly bound to the underlying distributed computing platforms, requiring users to be familiar with distributed computing. To improve the usability of distributed graph processing, we propose a unified distributed graph programming framework UniGPS. Firstly, we propose a unified cross-platform graph programming model VCProg for UniGPS. VCProg hides details of distributed computing from users. It is compatible with the popular graph programming models Pregel, GAS, and Push-Pull. VCProg programs can be executed by compatible distributed graph processing systems without modification, reducing the learning overheads of users. Secondly, UniGPS supports Python as the programming language. We propose an interprocess-communication-based execution environment isolation mechanism to enable Java/C++-based systems to call user-defined methods written in Python. The experimental results show that UniGPS enables users to process big graphs beyond the memory capacity of a single machine without sacrificing usability. UniGPS shows near-linear data scalability and machine scalability.
EcoTransLearn: an R-package to easily use transfer learning for ecological studies—a plankton case study

In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training data. To compensate the lack of 'easy-to-use' software optimized for ecological studies, we propose the EcoTransLearn R-package, which allows greater automation in the classification of images acquired with various devices (FlowCam, ZooScan, photographs, etc.), thanks to different TL methods pre-trained on the generic ImageNet dataset.EcoTransLearn is an open-source package. It is implemented in R and calls Python scripts for image classification step (using reticulate and tensorflow libraries). The source code, instruction manual and examples can be found at https://github.com/IFREMER-LERBL/EcoTransLearn.Supplementary data are available at Bioinformatics online.
A Prototype-Based Neural Network for Image Anomaly Detection and Localization

Abstract Image anomaly detection and localization perform not only imagelevel anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in various fields. This paper proposes ProtoAD, a prototypebased neural network for image anomaly detection and localization. First, the patch features of normal images are extracted by a deep network pre-trained on nature images. Then, the prototypes of the normal patch features are learned by non-parametric clustering. Finally, we construct an image anomaly localization network (ProtoAD) by appending the feature extraction network with the L2 feature normalization, a 1 × 1 convolutional layer, a channel max-pooling, and a subtraction operation. We use the prototypes as the kernels of the 1 × 1 convolutional layer; therefore, our neural network does not need a training phase and can conduct anomaly detection and localization in an end-to-end manner. Extensive experiments on the MVTec AD dataset demonstrate that ProtoAD achieves competitive performance compared to the state-of-the-art methods with a higher inference speed. The source code will be released on GitHub.
Learning to Answer Visual Questions from Web Videos

Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets. To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and the VideoQA feature probe evaluation setting and show excellent results, in particular for rare answers. Furthermore, our method achieves competitive results on MSRVTT-QA, ActivityNet-QA, MSVD-QA and How2QA datasets. We also show that our VideoQA dataset generation approach generalizes to another source of web video and text data. We use our method to generate the WebVidVQA3M dataset from the WebVid dataset, i.e., videos with alt-text annotations, and show its benefits for training VideoQA models. Finally, for a detailed evaluation we introduce iVQA, a new VideoQA dataset with reduced language bias and high-quality manual annotations. Code, datasets and trained models are available at https://antoyang.github.io/just-ask.html
Relational Triple Extraction: One Step is Enough

Extracting relational triples from unstructured text is an essential task in natural language processing and knowledge graph construction. Existing approaches usually contain two fundamental steps: (1) finding the boundary positions of head and tail entities; (2) concatenating specific tokens to form triples. However, nearly all previous methods suffer from the problem of error accumulation, i.e., the boundary recognition error of each entity in step (1) will be accumulated into the final combined triples. To solve the problem, in this paper, we introduce a fresh perspective to revisit the triple extraction task, and propose a simple but effective model, named DirectRel. Specifically, the proposed model first generates candidate entities through enumerating token sequences in a sentence, and then transforms the triple extraction task into a linking problem on a "head $\rightarrow$ tail" bipartite graph. By doing so, all triples can be directly extracted in only one step. Extensive experimental results on two widely used datasets demonstrate that the proposed model performs better than the state-of-the-art baselines.
Augmenting Poetry Composition with Verse by Verse

We describe Verse by Verse, our experiment in augmenting the creative process of writing poetry with an AI. We have created a group of AI poets, styled after various American classic poets, that are able to offer as suggestions generated lines of verse while a user is composing a poem. In this paper, we describe the underlying system to offer these suggestions. This includes a generative model, which is tasked with generating a large corpus of lines of verse offline and which are then stored in an index, and a dual-encoder model that is tasked with recommending the next possible set of verses from our index given the previous line of verse.
Igniting Growth Surges: Lessons from the Past

Abstract ``What, exactly" can countries do to initiate an episode of growth surge? I identify 132 episodes of growth surges in 117 countries over the period 1980-2010. I find that disproportionate improvements in macroeconomic stability and external factors and endowments favor a higher probability of growth surge. They are followed by structural reforms, investments, labor and productivity, trade diversification and quality, and lastly by institutions. Countries can maximize the likelihood of igniting growth surges if they jointly achieve significant improvements in macroeconomic stability and external conditions and endowments. Also, macroeconomic stability, and to some extent, external factors and endowments may be considered as dominant strategies to ignite a growth surge, as no improvements in these determinants, generally constraint the other determinants to have a smaller effect on growth surges. There is a notable difference between decades, regions, and levels of development, which calls for careful tailoring of policies aiming at igniting growth surges to local conditions. JEL Classification: O11 , O47 , F43 , E65
The institutional repository of the Municipal Library “B.P. Hasdeu”: keyword frequency analysis

The exercise of selecting keywords from a (scientific) article has become a mandatory process for scientific publications. In specialized journals until 2000, the procedure was occasionally practiced, especially in studies published abroad. Interest in keywords has exploded with the development of the Internet, and more recently with the development of the concept of open science. Together with the actual title, but also with the summary (summary, abstract) of a scientific work, its supporting words, if they are well chosen, increase the chances of being read by as many specialists as possible (also increasing the probability of being cited) and to be put more quickly in the scientific circuit. Search engines, databases or websites (not only those of specialized magazines) use keywords to maximally synthesize the information, identify the source more quickly and make it more accessible. Municipal Library “B.P. Hasdeu” from Chisinau launched in 2021 its institutional repository with the acronym HAPEs (https://hapes.hasdeu.md/), developed on the DSpace platform. Although short, the experience of managing this repository can be improved by analyzing the constituent elements and contents of the collections. For our study we chose the “keywords” component, which we consider valuable for effective digital content management and which ideally reflects Zipf's law.
Challenges and opportunities in open scientific data policy development in the Republic of Moldova

Open science initiatives are a global phenomenon. If in developed countries research and innovation enjoy significant support from governments, business and the entire society, and open science has become an important component in the landscape of scientific research, developing countries, such as the Republic of Moldova, have a situation a little more special regarding open access to scientific information, the use of new technologies and tools in the sharing and dissemination of knowledge, in the broad participation and collaboration between social partners in the act of research and innovation. New achievements in the field of information and communication technology offer developing countries great opportunities to increase their research capabilities and participate in the development of world science. There are, however, certain specific barriers that still prevent the free circulation of scientific information, the broad collaboration of actors in the field of research. Ensuring open access to research data is a big challenge for the scientific community in the Republic of Moldova. In order to respond to this challenge, it is necessary to develop and implement some policies and some normative acts that contribute to the consolidation of efforts in order to identify and capitalize on the opportunities offered by new technologies, good practices and European and international experience, the establishment of some sustainable mechanisms regarding management and reuse of research data. The paper addresses the issue of open scientific data in the Republic of Moldova emphasizing the need to develop policies that take into account local realities but also the opportunities for international collaboration and, first of all, those related to the status of a candidate country for joining the Union European.
1 Fieldwork techniques in semantics

This chapter focuses on techniques to be used in semantics fieldwork. More specifically, we discuss complementary techniques, such as the use of storyboards to contextualize elicitations, the implementation of training sessions and control sentences, and the use of Google Forms. These techniques are to be implemented alongside traditional methods, such as questionnaires, contextualized translations, truth judgment tasks, and storyboards. Good linguistic analysis heavily depends on the quality of the data collected during fieldwork. There are two kinds of factors that may impair the quality of the data. The first includes factors inherent to the methodology, such as the use of contexts in order to determine the exact truth conditions of a sentence. The great number and variety of contexts tends to make consultants feel fatigued or bored during elicitation sessions. The second kind of factors that may lead to poor quality data relates to external conditions. Examples of these are consultants having a bad day or failing to understand instructions because they are not fluent speakers of the contact language. Being prepared for these kinds of problems increases the chances of successful fieldwork. This chapter argues that the combination of different methods yields more reliable fieldwork results. We contextualize our argument by presenting data we have collected during fieldwork that turned out to be unreliable. We also argue that the implementation of the suggested complementary techniques has a positive impact on fieldwork sessions. It increases consultants’ attention level and furnishes important feedback on how much we can rely on data from each particular consultant.
A time-series analysis of vocabulary in Japanese texts: Non-characteristic words and topic words

In this study, I analyzed the distribution of words in the text from a time-series perspective. The data were comprised of 635 texts, where a token ranges from 1950-2050 words from the Balanced Corpus of Contemporary Written Japanese. Each text was divided into 10 segments containing an equal number of words, and the distribution of words among them was investigated. The relationship between the frequency of appearances and the characteristics of the words was also analyzed. From the results, the following conclusions were drawn. (1) The distribution of words among the segments follows a decreasing curve, like a Zipf’s curve, but starts to rise close to the end of the curve. (2) At the token level, as the word appearance ratio increases, the ratio of particles increases, and the ratio of nouns decreases. Additionally, the ratio of auxiliary verbs becomes slightly higher, and there is no considerable change in the ratio of verbs. (3) Conversely, at the type level, the proportion of parts of speech remains almost unchanged. (4) The average number of words that appear in all segments was about 12 words per text, and there was no significant difference between the registers. (5) Four hundred and seventy different words appeared in all segments. They were divided into topic words, scene words, function words, and noncharacteristic words from the discourse structure point of view, and were classified according to the number of text appearances.
COVID-19 contact tracing apps in Europe, technological feat or failure?

Abstract Background In the context of infectious disease control (IDC), public health services (PHS) have been under great pressure during the COVID-19 pandemic, especially with the burden of contact tracing. Most European Union countries have developed contact tracing apps for smartphones (CTA) with the goal of aiding PHS in IDC. CTAs generally use proximity data from mobile devices to determine a user's risk of exposure to SARS-CoV-2, thus providing testing and isolation recommendations. This review aimed to study the acceptance and adoption of CTAs in Europe. Methods 5 European countries were selected: Germany, Spain, France, Ireland, Italy. A literature review was carried out and official sources of information from each country were consulted in order to compare the adoption of each national CTA. Criterias included number of downloads, rating in the app stores, costo of development, proportion of positive tests registered. Potential factors influencing population adherence to CTAs were also investigated. Results In proportion to their population, the number of downloads varied significantly in each country (18 % in Spain, 67% in France, 75% in Ireland). Except for Spain, all countries integrated additional functions into the CTA to increase its uptake, such as access to the vaccination certificate. App stores ratings ranged from 3.0 (Spain) to 3.9 (France and Ireland). The proportion of tests added in the apps varied significantly (1% in Italy and Spain, 4% in Ireland, 10% in France and 17% in Germany). Concerns that lead to the non-use of CTAs were related to data protection, smartphone battery drainage and app bugs. Conclusions CTAs as a way to identify contacts from a positive case had a low impact in the analyzed countries, with low population adherence being an important factor. Adding more features within the apps, minimizing bugs, and increasing public confidence in data privacy seem essential to increase uptake in the future. Key messages