Reviewerly/exHarmony huggingface dataset:
A Russian Jeopardy! Data Set for Question-Answering Systems

Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much appreciated in chatbots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! - "Own Game". We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA competition based on the data set collected from this database.
Resource Theories as Quantale Modules

We aim to counter the tendency for specialization in science by advancing a language that can facilitate the translation of ideas and methods between disparate contexts. The focus is on questions of "resource-theoretic nature". In a resource theory, one identifies resources and allowed manipulations that can be used to transform them. Some of the main questions are: How to optimize resources? What are the trade-offs between them? Can a given resource be converted to another one via the allowed manipulations? Because of their ubiquity, methods used to answer them in one context can be used to tackle corresponding questions in new contexts. The translation occurs in two stages. Firstly, methods are generalized to the abstract language. Then, one can determine whether potentially novel contexts can accommodate them. We focus on the first stage, by introducing two variants of an abstract framework in which existing and yet unidentified resource theories can be represented. Using these, the task of generalizing concrete methods is tackled in chapter 4 by studying the ways in which meaningful measures of resources may be constructed. One construction expresses a notion of cost (or yield) of a resource. Among other applications, it may be used to extend measures from a subset of resources to a larger domain. Another construction allows the translation of resource measures from one resource theory to another. Special cases include resource robustness and weight measures, as well as relative entropy based measures quantifying minimal distinguishability from freely available resources. We instantiate some of these ideas in a resource theory of distinguishability in chapter 5. It describes the utility of systems with probabilistic behavior for the task of distinguishing between hypotheses, which said behavior may depend on.
Analisis Taksonomi pada Soal Asesmen Kompetensi Minimum Literasi Membaca di SMK Negeri 3 Pekanbaru

The Minimum Competency Assessment (AKM) measures literacy achievement to map the quality of education in Indonesia with international standards. This AKM aims to identify student achievements when working on AKM questions in terms of the reading literacy process. The importance of AKM training is to improve teacher skills in developing learning frameworks that lead to students' reasoning in literacy. This study aims to determine the cognitive level of reading literacy AKM at SMK Negeri 3 Pekanbaru. This research method is content analysis with a qualitative approach. The analysis uses qualitative methods to interpret the cognitive level of the items. Based on the study results, the instrument question classification I percentage was 80% for C2 (Understanding); and 20% for C4 (Analysis). The percentage of instrument II questions is 74% for C2 (Understanding), 22% for C4 (Analysis), and 3% for C6 (Create). The cognitive level dominates the lower level, namely C2 (Understanding). While cognitive levels such as C1 (Remembering), C3 (Applying), C4 (Analyzing), C5 (Creating), and C6 (Evaluating) were found to be very few and had not even appeared in the AKM Reading Literacy question at SMK Negeri 3 Pekanbaru.
Supporting Students' Self-Directed Learning in EFL Speaking Classroom by Using Cake Application

Students are currently on the verge of a technological revolution known as Industry 4.0. As a result, it is critical for today's educators to be able to integrate a teaching-learning method with educational technology as appropriate media in order to achieve the goal of effectively teaching and learning EFL speaking. Smartphones or mobile devices can be loaded with a variety of language-learning apps. One of them is the Cake application, which will serve as the focal point of this current study to supplement previous relevant research. Using a case study research design, 25 third-semester English education program students as the research participants were asked to explore Cake at home for a week while recording all of their activities and discoveries. They were then given open-ended questionnaires to express their perceptions with the app. The results demonstrated that the Cake supported students' self-directed learning in EFL speaking classroom since it was easy, simple, interesting to use, and contained a lot of features for English learning independently, so they could manage themselves to decide the learning goals of using the app and control the learning process. The app could also be used to help them improve some components of speaking skills (pronunciation, vocabulary and grammar) which are the most common issues they face when speaking English. As a result, this app could be a good way for EFL students to improve their speaking skills and support their self-directed learning.
RSVN: A RoBERTa Sentence Vector Normalization Scheme for Short Texts to Extract Semantic Information

With the explosive growth in short texts on the Web and an increasing number of Web corpora consisting of short texts, short texts are playing an important role in various Web applications. Entity linking is a crucial task in knowledge graphs and a key technology in the field of short texts that affects the accuracy of many downstream tasks in natural language processing. However, compared to long texts, the entity-linking task of Chinese short text is a challenging problem due to the serious colloquialism and insufficient contexts. Moreover, existing methods for entity linking in Chinese short text underutilize semantic information and ignore the interaction between label information and the original short text. In this paper, we propose a RoBERTa sentence vector normalization scheme for short texts to fully extract the semantic information. Firstly, the proposed model utilizes RoBERTa to fully capture contextual semantic information. Secondly, the anisotropy of RoBERTa’s output sentence vectors is revised by utilizing the standard Gaussian of flow model, which enables the sentence vectors to more precisely characterize the semantics. In addition, the interaction between label embedding and text embedding is employed to improve the NIL entity classification. Experimental results demonstrate that the proposed model outperforms existing research results and mainstream deep learning methods for entity linking in two Chinese short text datasets.
The Receptivity to Safety-Related Mobile Apps Among Commercial Fishing Captains: Descriptive Exploratory Study

Mobile apps addressing a variety of workplace safety issues have proliferated over the last decade as mobile technology has advanced and smartphone ownership has increased. Workplace safety interventions are often designed for a specific work site. However, some of the most dangerous jobs are ones in which workers frequently change field locations, such as commercial fishing. Mobile apps may be particularly suitable for delivering safety interventions to these workers.We sought to gauge the potential for using mobile apps to deliver safety interventions to commercial fishing workers. The purpose of this paper is to describe how fishermen use their mobile devices during fishing operations and identify any mobile apps they already use for safety.Participants comprised commercial fishing captains who already owned an iOS or Android smartphone or tablet. They completed a questionnaire that asked about their current mobile device use and their use of safety-related mobile apps, in addition to questions about their fishing operations. We performed descriptive analyses of the data.A total of 61 participants completed the questionnaire. The most common types of mobile devices participants reported owning were iPhones (n=36, 59%) and Android phones (n=24, 39%). Most participants (n=53, 87%) reported using their mobile device for both work and personal purposes, including while out at sea (n=52, 85%). Over half of the participants reported that they had either safety-related apps (n=17, 28%) or apps that help them with their work (n=35, 57%). The types of apps most frequently mentioned were apps for weather, wind, tides, and navigation.The results of this study indicate that some commercial fishing captains who own a mobile device are receptive to using safety-related apps for work. Apps that help avoid hazards by monitoring environmental conditions and apps optimized for use on smartphones may be most likely to be adopted and used. Overall, these results suggest that mobile apps are a promising avenue for improving safety among workers in commercial fishing and similar occupations.
Open Access Monographs: Making Mandates Reality

Watch the VIDEO. In early 2022 the major research funding body in the UK (UKRI), released a policy statement mandating Open Access (OA) monographs, with an implementation date of January 1st, 2024. This date will see a shift from a hypothetical future for OA books to a concrete policy with a hard deadline. Add to this the forthcoming mandates on OA books from cOAlition S/Plan S in Europe, and potential implications of the next research impact evaluation (REF) in the UK, and it is clear that there is a pressing need for libraries and academic book publishers to understand how the sector will meet the challenges of implementing these policies. In this session we will present the following questions: what is the transition to open access for scholarly books going to look like? What pitfalls and opportunities will it bring? Who is at risk of being excluded? How do we get from where publishing and libraries are today, to making these policies a workable reality in 2024? We will also pose the crucial question: how will this transition be paid for? We’ll demonstrate how Book Processing Charges (BPCs) worsen inequality by favouring the most wealthy institutions and authors. And we’ll highlight some collective library funding models that seek to spread the funding of OA books so that no single institution bears a disproportionate cost. These models range from the large and well-funded like MIT’s Direct 2 Open and Michigan’s Fund to Mission, to the COPIM project’s own Opening the Future model which is aimed at smaller mission-driven publishers like CEU Press and Liverpool University press. With the clock ticking on imminent policies, conversation is urgently needed on the practicalities of making mandates reality. We’ll discuss what libraries can do to meet the challenge, and how supporting programmes like Opening the Future will ensure that the transition to OA is a sustainable and bibliodiverse one. The transition to OA should not leave smaller and medium-sized presses behind; nor should it rely on paying BPCs, which risks excluding any author without funding; and the transition must be sustainable for libraries. We will demonstrate that the time to support collective funding programme pilots is now, in order that they can transition from pilot to implementable policy, to the benefit of all: opening up research for the public good and building an open, global ‘collection’ that is shared by libraries in common, around the world.
C2ShadowGAN: cycle-in-cycle generative adversarial network for shadow removal using unpaired data

Abstract Recent advances in deep learning technology, and the availability of public shadow image datasets, have enabled significant performance improvements of shadow removal tasks in computer vision. However, most deep learning-based shadow removal methods are usually trained in a supervised manner, in which paired shadow and shadow-free data are required. We developed a weakly supervised generative adversarial network with a cycle-in-cycle structure for shadow removal using unpaired data. In addition, we introduced new loss functions to reduce unnecessary transformations for non-shadow areas and to enable smooth transformations for shadow boundary areas. We conducted extensive experiments using the ISTD and Video Shadow Removal datasets to assess the effectiveness of our methods. The experimental results show that our method is superior to other state-of-the-art methods trained on unpaired data.
Bangla Sign Language Gesture Recognition System

Sign language is a form of communication used by the deaf and dumb. However, integrating them into society is challenging because most people do not speak their language. It is a method of generating and disseminating ideas, information, points of view, facts, and sentiments to establish a shared understanding. Unfortunately, almost 5% of the world's population is not blessed with the ability of verbal communication. The situation is the same for Bangladesh. This paper presents a simple, low-cost Bangla sign language (BdSL) recognition model with maximum accuracy to convert signs into Bangla text. We've manually captured images for Bangla sign language following the BdSL model and implemented the dataset to train the system and show output. We used neural networks as a deep learning method to train individual signs to achieve our goals in the suggested model. Using a CNN, we separated the dataset into two parts: training data and test data. The action is mapped to the relevant text in the training data using image processing techniques, and neural networks, so raw images or videos are turned into text that can be read and comprehended with accuracy near 92%. Finally, the system will turn the user's signs and motions into text. It is referred to as "sign language recognition." In the future, This research can also open doors to numerous other applications, like sign language tutorials or dictionaries, and can also help the deaf and dumb search the web or send emails more conveniently. We hope this system will be an excellent opportunity for people who have speaking or hearing disabilities by helping them explore more.
APPROACHES TO THE CONTENT IMPROVEMENT OF THE «DIGITAL TECHNOLOGIES IN THE PROFESSIONAL ACTIVITY» DICIPLINE UNDER THE CONDITIONS OF THE FORCED DISTANCE TEACHING

The article examines the problem of improving the educational process of studying ICT in the preparation of masters in the educational and professional program «Professional Education. Computer technologies» at Berdyansk State Pedagogical University. The improvement and clarification of the content of the discipline «Digital technologies in professional activity» was considered, which should be based on the consideration in the educational process of professional education of today's challenges of ICT application by future specialists. The application of six main categories of ICT, which are actively used in the educational process, has been analyzed. Training ICT, which have been designed to consolidate knowledge, skills and abilities. Cognitive ICT are focused on mastering concepts that serve to ensure the organization of the educational process in accordance with programmed learning systems. ICT for problem-based learning are focused on the implementation of educational and cognitive tasks and the principles of indirect management of the educational process. Simulating and modeling ICT, which are intended for simulating and modeling various processes, phenomena, situations of human social life, etc. Game ICT are used as a form and method to achieve certain educational goals. Reference and information ICTs provide access to knowledge bases, dictionaries, encyclopedias, information and search systems, etc. These categories are the basis for improving the content of the «Digital technologies in professional activity» discipline. A confirmatory study was conducted among stakeholders, owing to which specific ICTs have been determined that should be included in the content of the educational component «Digital technologies in the professional activity». The resulting list of ICT is quite broad, but it should be noted that a significant part of ICT can be used to achieve various educational goals and solve various didactic tasks, including: Google Keep, LearningApps, MindMeister, Nearpod, OneNote Web Clipper, Pear Deck, Popplet and TheBrain. A separate group is ICT that represent a whole range of tools and technologies and are system ICT, such as: Canvas, Google Classrom and LMS Moodle. Key words: information and communication technologies, content of education, distance education, professional education, stakeholders.
CG-NeRF: Conditional Generative Neural Radiance Fields

While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics. In this paper, we propose a novel model, referred to as the conditional generative neural radiance fields (CG-NeRF), which can generate multi-view images reflecting extra input conditions such as images or texts. While preserving the common characteristics of a given input condition, the proposed model generates diverse images in fine detail. We propose: 1) a novel unified architecture which disentangles the shape and appearance from a condition given in various forms and 2) the pose-consistent diversity loss for generating multimodal outputs while maintaining consistency of the view. Experimental results show that the proposed method maintains consistent image quality on various condition types and achieves superior fidelity and diversity compared to existing NeRF-based generative models.
More-than-Human-Data Interaction

Our everyday interactions with numerous smart devices, digital services and the Internet are creating enormous volumes of data. Almost half of the data created globally is now said to originate from Internet of Things and Artificial Intelligence (IoT-AI) systems. Further, the processing of such data and its transmission from the ‘Edge of computing networks’ to ‘the Cloud’ and back again, is significantly contributing to ICT's carbon footprint of almost 4% of global CO2 emissions. These emissions are increasingly affecting the planet's natural environment as they increase the Earth's temperature and contribute to climate change. Crucially however, the environmental impact of the data collected and produced through IoT-AI is rarely considered within the design process as the perceived potential monetary value of said data dominates. Current Human-Data Interaction (HDI) theory and practice aims to help to make ongoing IoT-AI data operations more legible to users as well as empower them with levels of agency to negotiate how their data is managed between themselves and technology providers. As such, HDI presently places primary emphasis on the needs of human actants. Given the urgent need to highlight and improve Data Sustainability, in this paper, we shift perspective and refocus data interactions in relation to the needs of ecological actants – flora, fauna and climate. We firstly describe the theoretical merging of current research methods HDI with More-than-Human-Centred Design to develop the conceptual scaffold for an approach we term More-than-Human Data Interaction. Using the relationship between IoT-AI data and Edge Computing as a context for such design activities, we outline how we concretised the notion of More-than-Human Data Interaction through the creation of Speculative Games. Based on these explorations, we put forward a series of More-than-Human Data Interaction considerations for designers and technologists to address when approaching the design and implementation of future IoT-AI data-driven products and services. Further, by framing the issue of Data Sustainability within such design research challenges, we aim to contribute to growing technological discourse related to Net Zero 2050 targets and UN Sustainable Development Goals.
Harry Potter and technopessimism: are «zoomers» ready for the technological progress?

The article reveals the conditions for the science fiction and fantasy fiction to affect formation of technooptimistic and technopessimistic attitude of young readers. Primarily, the work assumes the way fiction heroes understand artefacts with reservations can be considered as the basis to reveal the attitude to engineering among those generations for whom these works were notional points. Technooptimism is defined as a worldview attitude within which nature and the humanbeing cause problems. The article shows that technopessimism identifies the human activity as the problem source. Harry Potter series proved to shape technopessimistic worldview and excuse technology inequality. Through the example of T. Pratchett books the article demonstrates that literature can formalize readers responsible attitude towards equipment if it shows not only the threat that pose technologies (the Promethan mentality), but also remedies to manage effects of machinery use, including those resulting from improper decisions (the Epimethian mentality).
Current Approaches for Image Fusion of Histological Data with Computed Tomography and Magnetic Resonance Imaging

Classical analysis of biological samples requires the destruction of the tissue’s integrity by cutting or grinding it down to thin slices for (Immuno)-histochemical staining and microscopic analysis. Despite high specificity, encoded in the stained 2D section of the whole tissue, the structural information, especially 3D information, is limited. Computed tomography (CT) or magnetic resonance imaging (MRI) scans performed prior to sectioning in combination with image registration algorithms provide an opportunity to regain access to morphological characteristics as well as to relate histological findings to the 3D structure of the local tissue environment. This review provides a summary of prevalent literature addressing the problem of multimodal coregistration of hard- and soft-tissue in microscopy and tomography. Grouped according to the complexity of the dimensions, including image-to-volume (2D ⟶ 3D), image-to-image (2D ⟶ 2D), and volume-to-volume (3D ⟶ 3D), selected currently applied approaches are investigated by comparing the method accuracy with respect to the limiting resolution of the tomography. Correlation of multimodal imaging could position itself as a useful tool allowing for precise histological diagnostic and allow the a priori planning of tissue extraction like biopsies.
BASDB: Blockchain assisted Secure Outsourced Database Search

The outsourcing of databases is very popular among IT companies and industries. It acts as a solution for businesses to ensure availability of the data for their users. The solution of outsourcing the database is to encrypt the data in a form where the database service provider can perform relational operations over the encrypted database. At the same time, the associated security risk of data leakage prevents many potential industries from deploying it. In this paper, we present a secure outsourcing database search scheme (BASDB) with the use of a smart contract for search operation over index of encrypted database and storing encrypted relational database in the cloud. Our proposed scheme BASDB is a simple and practical solution for effective search on encrypted relations and is well resistant to information leakage against attacks like search and access pattern leakage.