Reviewerly/exHarmony huggingface dataset:
POSSITIONING OG HIGHER EDUCATION INSTITUTIONS ON THE MARKET OF EDUCATIONAL SERVICES

For higher education institution, one of the most important tasks is to form its own competitive advantages, search, interest and retain consumers, because the main goal of HEIs is its long-term successful activity based on the quality of educational services. On the one hand, the transition to market relations has contributed to the emergence of nongovernmental (private) higher education institutions, increasing the number of participants and a variety of educational programs. On the other hand, the demand for higher education services has risen sharply. As a result, competition in the education market has intensified, which has prompted higher education institutions to increasingly use marketing to adapt their activities to market conditions. The complexity of positioning a higher education institution is determined by the diversity of consumers of educational services, including employers as representatives of the labor market. Therefore, the task of the educational organization is to find a unique position that, being significant for all or most consumers, would allow the higher education institution to differentiate itself from competitors. The article investigates the directions of positioning of higher education institutions. It is established that as a result of increasing competition in the market of educational services, there is a need for higher education institutions to increasingly use marketing to adapt their activities to market conditions. The essence of the definition of "positioning" from the point of view of different authors is considered, the concept of positioning through the prism of higher education institutions is singled out. The criteria for selecting higher education ЕКОНОМІКА ТА УПРАВЛ ІННЯ НАЦІОНАЛЬНИМ ГОСПОДАРСТВОМ 42 Випуск І (81), 2021 institutions by potential entrants (economic, social, organizational), generalized a survey of respondents to identify areas of positioning (ownership, educational programs, demand for graduates in the labor market, interaction with the business environment, additional services to consumers, staff , opportunities for comprehensive personal development, cost).
Offline Reinforcement Learning with Reverse Model-based Imagination

In offline reinforcement learning (offline RL), one of the main challenges is to deal with the distributional shift between the learning policy and the given dataset. To address this problem, recent offline RL methods attempt to introduce conservatism bias to encourage learning on high-confidence areas. Model-free approaches directly encode such bias into policy or value function learning using conservative regularizations or special network structures, but their constrained policy search limits the generalization beyond the offline dataset. Model-based approaches learn forward dynamics models with conservatism quantifications and then generate imaginary trajectories to extend the offline datasets. However, due to limited samples in offline dataset, conservatism quantifications often suffer from overgeneralization in out-of-support regions. The unreliable conservative measures will mislead forward model-based imaginations to undesired areas, leading to overaggressive behaviors. To encourage more conservatism, we propose a novel model-based offline RL framework, called Reverse Offline Model-based Imagination (ROMI). We learn a reverse dynamics model in conjunction with a novel reverse policy, which can generate rollouts leading to the target goal states within the offline dataset. These reverse imaginations provide informed data augmentation for the model-free policy learning and enable conservative generalization beyond the offline dataset. ROMI can effectively combine with off-the-shelf model-free algorithms to enable model-based generalization with proper conservatism. Empirical results show that our method can generate more conservative behaviors and achieve state-of-the-art performance on offline RL benchmark tasks.
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief

Although pretrained language models (PTLMs) contain significant amounts of world knowledge, they can still produce inconsistent answers to questions when probed, even after specialized training. As a result, it can be hard to identify what the model actually “believes” about the world, making it susceptible to inconsistent behavior and simple errors. Our goal is to reduce these problems. Our approach is to embed a PTLM in a broader system that also includes an evolving, symbolic memory of beliefs – a BeliefBank – that records but then may modify the raw PTLM answers. We describe two mechanisms to improve belief consistency in the overall system. First, a reasoning component – a weighted MaxSAT solver – revises beliefs that significantly clash with others. Second, a feedback component issues future queries to the PTLM using known beliefs as context. We show that, in a controlled experimental setting, these two mechanisms result in more consistent beliefs in the overall system, improving both the accuracy and consistency of its answers over time. This is significant as it is a first step towards PTLM-based architectures with a systematic notion of belief, enabling them to construct a more coherent picture of the world, and improve over time without model retraining.
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization

This paper investigates the problem of computing the equilibrium of competitive games, which is often modeled as a constrained saddle-point optimization problem with probability simplex constraints. Despite recent efforts in understanding the last-iterate convergence of extragradient methods in the unconstrained setting, the theoretical underpinnings of these methods in the constrained settings, especially those using multiplicative updates, remain highly inadequate, even when the objective function is bilinear. Motivated by the algorithmic role of entropy regularization in single-agent reinforcement learning and game theory, we develop provably efficient extragradient methods to find the quantal response equilibrium (QRE) -- which are solutions to zero-sum two-player matrix games with entropy regularization -- at a linear rate. The proposed algorithms can be implemented in a decentralized manner, where each player executes symmetric and multiplicative updates iteratively using its own payoff without observing the opponent's actions directly. In addition, by controlling the knob of entropy regularization, the proposed algorithms can locate an approximate Nash equilibrium of the unregularized matrix game at a sublinear rate without assuming the Nash equilibrium to be unique. Our methods also lead to efficient policy extragradient algorithms for solving entropy-regularized zero-sum Markov games at a linear rate. All of our convergence rates are nearly dimension-free, which are independent of the size of the state and action spaces up to logarithm factors, highlighting the positive role of entropy regularization for accelerating convergence.
Dual Parameterization of Sparse Variational Gaussian Processes

Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we improve their computational efficiency by using a dual parameterization where each data example is assigned dual parameters, similarly to site parameters used in expectation propagation. Our dual parameterization speeds-up inference using natural gradient descent, and provides a tighter evidence lower bound for hyperparameter learning. The approach has the same memory cost as the current SVGP methods, but it is faster and more accurate.
AI Process Description Algebras and Ontology Preservation Techniques

Tree computing models with ontology preserving functions are applied to transform learning across domains with structural morphisms (first author 1992–2005). A computing model based on novel competitive learning with multiplayer game tree planning is applied where computing agents compare competing models to reach goal plans. Goals are satisfied based on competitive game tree learning. The techniques are example prototypes for modeling ontology algebras. Example Applications to data filtering, robotics processes, class hierarchical transaction morphisms, exchanges are the newest areas. Modeling frameworks are the natural applications for our event process algebras. Our techniques allow modeling learning across class ontologies and process models based on agent computing structural morphisms to model the ontology transfer processes. Applications to Formal concept description is developed with new description logic algebraic models. Novel concept description ontology algebras and concept description ontology preservation morphisms are presented. Based on that new concept ontology algebras with description ontology algebra preservation theorem are proved.
BayesIMP: Uncertainty Quantification for Causal Data Fusion

While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. As data arises from multiple sources and can vary in quality and quantity, principled uncertainty quantification becomes essential. To that end, we introduce Bayesian Interventional Mean Processes, a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space, while taking into account the uncertainty within each causal graph. To demonstrate the utility of our uncertainty estimation, we apply our method to the Causal Bayesian Optimisation task and show improvements over state-of-the-art methods.
Science and Technology ODA Promotion of Korea through ICT of Global Problem Solving Centers -Suggestion on the mid- and short-term projects promotion of science and technology ODA roadmap-

The Korean government proposed the K-SDGs in 2019 to promote the UN SDGs, but the role and tasks of science and technology, an important means of implementing the SDGs, have not been materialized. Accordingly, the role of science and technology ODA for the SDGs was established through the Ministry of Science and ICT's policy research project 'Science and Technology ODA Promotion Roadmap for Spreading the New Southern Policy and Realizing the 2030 SDGs'. In addition, goals, strategies, and core tasks for the next 10 years were derived in 10 fields such as water, climate change, energy, and ICT. In this paper, we analyze 30 key tasks of the ODA promotion roadmap for science and technology for the realization of SDGs, and propose mid- and short-term tasks and implementation plans for effective roadmap promotion. Among the key tasks in each field, four common elements were derived: ICT/smartization, a global problem-solving center, cooperation/communication platform, and business model/startup support platform/living lab that can create and integrate roadmap implementation conditions. In addition, the four mid- and short-term tasks, 1) Establishment of science and technology ODA network, 2) Establishment of living lab business platform linked to start-up support business, 3) Local smartization of recipient countries, and 4) Expand and secure sustainability of global problem-solving centers, were set in relation to the implementation of the detailed roadmap. For the derived mid- and short-term tasks, detailed implementation plans based on the ICTization of global problem-solving centers were presented. The implementation of the mid- and short-term tasks presented in this paper can contribute to the more effective achievement of the science and technology ODA roadmap, and it is expected that Korea's implementation of SDGs will also achieve high performance.
A Study on the Strategy of Non-face-to-face Writing Lectures in the Post-Corona Era －Based on PBL-based Thinking and Expression

The liberal arts lecture called “thinking and expression” aims to provide a foundation for improving basic and professional communication skills to be used in universities and societies.&lt;br/&gt;However, it is true that “thinking and expression” is being developed as a writing-oriented lecture at each university, and online lectures in the current non-face-to-face situation expose various limitations due to the nature of writing. As a result, the following conclusions were obtained by reviewing the PBL lecture module of K University.&lt;br/&gt;First, theory-oriented writing classes do not stimulate students' interest in non-face-to-face situations, and existing writing textbooks also need to be extensively supplemented suitable for non-face-to-face situations. In non-face-to-face situations, writing lectures face contradictions that have no choice but to return to theory rather than practice, so it is imperative to develop online writing lecture modules in the university itself to improve the quality of lectures.&lt;br/&gt;Second, discussion classes based on PBLs are a priority to forestall various problems that can occur in non-face-to-face classes. It is important to consider using the video conference platform to facilitate communication between students in non-face-to-face situations as much as possible, but to encourage direct conversation to build intimacy rather than chatting. In order to become a PBL differentiated from the existing group learning, we pay more attention to the problem-solving process itself, and lead cooperative learning to more actively respond to social problems and find practical solutions through the ‘living lab’ method.&lt;br/&gt;Third, in non-face-to-face writing, the interaction between students and professors is absolute, and the process of writing and correcting together is important to form communication. Along with real-time lectures using video conferencing platforms, it is very important for professors to continue to show interest in students' writings and feedback them one by one.
Password Generation Method from Color Image using the DCT Sign of RGB Component

The background of this research is to reduce the burden on users for password management with the spread of information devices such as smartphones. The purpose of this research is to generate a unique password character string that reflects the change in color tone in the image using the RGB component. The proposed method extracts the DCT sign of the RGB component by zigzag scan and generates a password character string. As a result, it was found that the proposed method is an image-specific password character string that reflects the difference in color tone.
Teaching strategies for information literacy training actions in school libraries

It aims to present teaching strategies that can contribute to the development of information literacy in the context of the school library. Firstly, it contextualizes information literacy regarding the relation between school library and school culture, and demonstrates how important it is for the librarian to own teaching techniques that may subsidize the development for information literacy. It contemplates the school librarian’s operation, pointing out that he lacks a better suiting formation for the pedagogical practice, but on the other hand, it evinces the educational functions practiced by the librarian, the educative aspect of his actions and how the pedagogical forms categorized as elementary and complex are applied in the library. Sequentially it highlights the relevance of collaborative work between librarians and teachers, which directly influences the formation of abilities concerning the use of information. The methodology employed is exploratory and descriptive research with qualitative approach and bibliographic outline. It concludes that taking into account the students’ goals and cognitive development is required before choosing which strategies will be applied, favoring methodologies that promote active student participation in the learning process. It highlights the relevance of integrating information literacy with the school curriculum, not ignoring the contribution of extracurricular actions and non-formal education to the development of all dimensions of information literacy.
More than newspapers

For most researchers Trove means digitised newspapers, but there are many strands to both its history and its use in research.
Factors Affecting Digital Marketing Success in Jordan

Exploring factors affecting digital marketing success gains a great importance among academics and practitioners. Hence, the aim of this study is first to identify factors affecting digital marketing success, and, second, to test the effects of customer-dependent and firm-originated factors on digital marketing success using filed data. Data were collected by a questionnaire distributed to a sample of marketing managers in food retailing firms, and analyzed using IBM SPSS 25.0 Amos 22.0. The results revealed that both customer-dependent and firm-originated factors have significant and positive effects on digital marketing success. Results were discussed in line with prior works. Conclusion, academic and managerial implications along with limitations and future research suggestions are reported.
STEM IN EDUCATION – WORKSHOP FOR RESOURCES AND IDEAS

The paper aims in presenting some practical ideas for implementing STEM approach in school. Main accent is set on two technologies – Ozobot and, Makey Makey. Concrete examples from pedagogical practice for developing algorithmic thinking and soft skills are presented.
Práticas de leitura e suas representações: contribuições da biblioteca escolar

The article aims to discuss reading practices and their representations in the educational field, analyzing the contributions of reading activities carried out in the school library, with regard, in particular, to the practice of school research and to the development of information competence in in learners. Methodologically, a bibliographic corpus is studied reviewing authors who are attentive to the history of reading and school research practices such as Burke (2005), Castrillon (2011), Chartier (1990, 2001, 2003), Milanesi (2013), Pesavento (2005), Brisola and Romeiro (2018), Demo (2002), Garcez (2006), Gasque (2013), Kuhlthau (2010), among others. This literature dialogues with examples from the empirical world of actions that can be carried out in the library, triangulated with classroom activities to encourage the appreciation for reading and consequently enhances the practice of school research. The analysis of the studied theoretical references points to the relevance of the school library as a learning space and its primary contribution to reading practices and school research. The latter is fundamental for the development of students' information competence.
Features of Household Solid Waste Object Recognition on Garbage Collector Robot (GACOBOT)

Solid waste or garbage is one of the problems that must be faced by the world's population so that life becomes more harmonious. Through a series of studies, a Garbage Collector Robot (GACOBOT) was created which is expected to help humans overcome this problem in terms of garbage collection. By adding a feature in the form of object recognition, the waste can be sorted by type so that it can be grouped and processed further. In this research, using the Support Vector Machine (SVM) classification method based on the feature extraction of the Histogram of Oriented Gradients (HOG) as the main method. Researchers used 14 pieces of data as training data and 10 pieces of data as test data. From the results of the tests that have been carried out, it has been obtained a success rate of 100% that the system has succeeded in separating waste into 2 types, namely plastic bag waste and glass bottle waste.
Development of Natural Sciences Student Worksheet with Guided Inquiry Orientation Based on Contextual Subjects for Classification and Material Change in Class VII Muhammadiyah Private Middle School Langsa City

This research is a type of research and development (Research and Development) which aims to determine the feasibility and effectiveness and responses of students regarding natural science student worksheet with contextual orientation-guided guided inquiry that has been developed. The development natural science student worksheet uses the procedure of developing a 4D (four-D) model. The development procedures used in the study include 4 stages: (1) define, (2) design, (3) develop, (4) disseminate. The subjects of this study were 36 students of class VII in Muhammadiyah Langsa Private Junior High School. Data collection instruments in the form of assessment sheets were given to material experts, learning design experts, linguists and teachers of Natural Sciences. Meanwhile, to see the students' responses the researchers distributed and analyzed the questionnaire sheets. The method used to analyze data is quantitative descriptive techniques that are expressed in the distribution of scores and rating scale categories. This development research produces a product that meets the validity of the content, presentation, language and appearance with an average of 3.83 with a very good category and includes valid criteria and is feasible and effective to be used to improve student learning outcomes including individual learning completeness reaches 88%, completeness classical learning reaches 100% with N-gain percentage reaching 0.78, learning achievement achievement reaches 88% and gets positive responses from students reaching 98% with very good category.
Age Classification: Comparison of Human vs Machine in Prompted and Spontaneous Speech

We report on the results of an investigation to classify speaker age in vocal utterances with state-of-the-art machine learning algorithms on a small data set. We compare results of manual measurement, i. e., supervised automated extraction of phonetically interpretable measures and observation (by hypothesis tests via procedures like regression analysis) with the outcomes of experiments based on recent machine learning. On isolated vowels the machine outperformed the human estimates.
Exploring the Research Utility of Fan-Created Data in the Japanese Visual Media Domain

Researchers wishing to study the Japanese visual media domain do not currently have access to a large set of descriptive data for analysis. To remedy this, the Japanese Visual Media Graph project (JVMG) seeks to build a knowledge graph consisting of descriptive metadata sourced from various online fan communities, described using RDF ontologies. To better understand how this informal, crowdsourced data can be both described using a formal ontologies, and made useful to researchers, this paper presents a summary of the properties of community data from a number of fan sites, and discusses the impact it has on the creation of a unified dataset and on possible research use cases. We find that the data sources are of high quality and coverage. They complement each other well and our central ontology should enable these connections between related resources across communities. Certain niche research topics are enabled by the use of community created data alone, but others encourage the incorporation of additional authoritative sources.