Reviewerly/exHarmony huggingface dataset:
THE IMPLEMENTATION EFFECTS OF LEARNER'S EMOTION RECOGNITION AND VISUALIZATION BASED ON BRAIN WAVES AND AUGMENTED REALITY

Carl Ransom Rogers (1983) attributed learning to the result of the interaction of learners' behavior, cognition and emotion, and emphasized the influence of emotion on learners' learning motivation and interest. However, in the traditional classroom teaching, teachers are unable to perceive the emotional state of multiple students effectively, and students do not often pay attention to their own emotions in the learning process. So it is difficult to adjust the students' state of learning in a timely manner, and the learning effect is difficult to guarantee. Therefore, it is very important for learners to have a good emotional state in classroom teaching. For students, It is difficult to accurately judge their own emotion while they are in a confused mood. Nowadays, brain waves have attracted more and more attention. By analysing the emotional information hidden in electroencephalogram data (Zhang & Wang, 2008), this technology can realize accurate emotional perception. In addition, because of the ability of visualizing abstract content (Cai, Wang, Yang, & Liu), and supporting learners' interaction, Augmented Reality (AR) has a powerful potential in visualizing complex emotional perception results. Therefore, in this study, we attempt to use Muse headband to collect brainwave data. Through the emotional representation of α, β, δ and θ, we developed an AR application to display the main emotion in front of students in the scene of virtual and real fusion. During the in-field learning activity, students can pay attention to the virtual character on the screen, and try to change their emotional state. The aim of this research focuses on the problems of inability to timely perceive multiple students' emotional states for teachers and difficulties in emotional regulation for students in traditional teaching. We sent pre-test and post-test questionnaires to college students, and recruited 18 subjects to participate in empirical experiments and interviews. From the questionnaires and emotional data, it is found that accurate emotion recognition and visualization have a high demand among students. In addition, this application based on AR and brain waves abtains a high accuracy and a good visual effect, and has a positive impact on helping learners understand their emotional states and adjust their emotions.
Learning Large-scale Network Embedding from Representative Subgraph

We study the problem of large-scale network embedding, which aims to learn low-dimensional latent representations for network mining applications. Recent research in the field of network embedding has led to significant progress such as DeepWalk, LINE, NetMF, NetSMF. However, the huge size of many real-world networks makes it computationally expensive to learn network embedding from the entire network. In this work, we present a novel network embedding method called "NES", which learns network embedding from a small representative subgraph. NES leverages theories from graph sampling to efficiently construct representative subgraph with smaller size which can be used to make inferences about the full network, enabling significantly improved efficiency in embedding learning. Then, NES computes the network embedding from this representative subgraph, efficiently. Compared with well-known methods, extensive experiments on networks of various scales and types demonstrate that NES achieves comparable performance and significant efficiency superiority.
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems

With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques for building a recommender system, Collaborative Filtering (CF) is the most popular and widespread approach. However, cold start and data sparsity are the fundamental challenges ahead of implementing an effective CF-based recommender. Recent successful developments in enhancing and implementing deep learning architectures motivated many studies to propose deep learning-based solutions for solving the recommenders' weak points. In this research, unlike the past similar works about using deep learning architectures in recommender systems that covered different techniques generally, we specifically provide a comprehensive review of deep learning-based collaborative filtering recommender systems. This in-depth filtering gives a clear overview of the level of popularity, gaps, and ignored areas on leveraging deep learning techniques to build CF-based systems as the most influential recommenders.
Query Answer Reformulation over Knowledge Bases

The answer of a query, submitted to a database or a knowledge base, is often long and may contain redundant data. The user is frequently forced to browse through a long answer or refine and repeat the query until the answer reaches a manageable size. Without proper treatment, consuming the answer may indeed become a tedious task. This article then proposes a process that modifies the presentation of a query answer to improve the quality of the user’s experience in the context of an RDF knowledge base. The process reorganizes the original query answer by applying heuristics to summarize the results and to select template questions that create a user dialog that guides the presentation of the results. The article also includes experiments based on RDF versions of MusicBrainz, enriched with DBpedia data, and IMDb, each with over 200 million RDF triples. The experiments use sample queries from well-known benchmarks.
Model Linkage Selection for Cooperative Learning

We consider a distributed learning setting where each agent/learner holds a specific parametric model and data source. The goal is to integrate information across a set of learners to enhance the prediction accuracy of a given learner. A natural way to integrate information is to build a joint model across a group of learners that shares common parameters of interest. However, the underlying parameter sharing patterns across a set of learners may not be a priori known. Misspecifying the parameter sharing patterns or the parametric model for each learner often yields a biased estimation and degrades the prediction accuracy. We propose a general method to integrate information across a set of learners that is robust against misspecifications of both models and parameter sharing patterns. The main crux is to sequentially incorporate additional learners that can enhance the prediction accuracy of an existing joint model based on user-specified parameter sharing patterns across a set of learners. Theoretically, we show that the proposed method can data-adaptively select the most suitable way of parameter sharing and thus enhance the predictive performance of any particular learner of interest. Extensive numerical studies show the promising performance of the proposed method.
Advertency and Suitability of the CSU Vision - Mission and College of Allied Health Sciences Goals and Objectives

The vision, mission, goals, and objectives of an educational institution serve as the guiding principles for the institution. The success of an institution is dependent on the unity of people's views and interests, which must be present both physically and philosophically in order for it to be successful. The values held by the individuals in the institution have an impact on their perspective of the world. They must work together to reconcile divergent viewpoints, identify common ground, and develop a unified vision and objective for the organization. Cagayan State University's vision and mission, as well as the goals and objectives of the College of Allied Health Sciences' bachelor's degree programs in medical laboratory science, respiratory therapy, and public health, were investigated in this study to determine the level of awareness of and acceptance among employees, students, and alumni, as well as members of the local community.General conclusions were reached that all three groups of respondents were well aware of and strongly agreed with the vision of Cagayan State University, the goals of the College of Allied Health Sciences, and the objectives of the Bachelor of Science in Medical Laboratory Science, Master of Science in Respiratory Therapy, and Master of Science in Public Health programs.
ONLINE REPUTATION OF LIBRARY IN TERMS OF COVID-19 PANDEMIC

Purpose of the Article. Represent innovative tools and approaches in the interaction of libraries with readers during lockdowns, which, in turn, has a positive effect on their reputation. Methodology. The application of such general scientific methods as analysis and synthesis, generalization and abstraction, description and comparison made it possible to identify relevant tools for the interaction of libraries with readers during lockdowns. The main approaches to scientific knowledge to achieve this goal were descriptive, systematic and modeling. The scientific novelty of the work is that based on the empirical method of observation, five years of practical experience of the author in the implementation of online marketing of the profile organization, with the help of professional materials, official blogs and, in fact, directly library resources. users even during the lockdown. Conclusions. Building a reputation and increasing the prestige of the library in the online environment and modern conditions in which humanity is - is a process that can increase the competitiveness of the library, the prestige of the library profession, and attract additional audience attention. Libraries also need to improve and modernize their image, join the current trends and popular and fashionable online resources and services, thus guaranteeing their users a decent quality of services and ensuring constant support of relations with them. Current trends in attracting audiences to libraries are changing along with technology (Zoom, Google tools, social networks, messengers), media formats (podcasts) and more.&#x0D; Keywords: library reputation, online library reputation, digitalization of libraries into a pandemic.
Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature

In this paper, we address reasoning tasks from open vocabulary Knowledge Bases (openKBs) using state-of-the-art Neural Language Models (NLMs) with applications in scientific literature. For this purpose, self-attention based NLMs are trained using a common sense KB as a source task. The NLMs are then tested on a target KB for open vocabulary reasoning tasks involving scientific knowledge related to the most prevalent chronic diseases (also known as non-communicable diseases, NCDs). Our results identified NLMs that performed consistently and with significance in knowledge inference for both source and target tasks. Furthermore, in our analysis by inspection we discussed the semantic regularities and reasoning capabilities learned by the models, while showing a first insight into the potential benefits of our approach to aid NCD research.
Assessing Institutional Capacity in Fostering Innovation in the GCC: A Comparative Analysis

The subject of the paper is the interrelationship between knowledge-based economies (KBEs), country-level innovation, and institutional capacities of governments as key to economic and social prosperity.
Optimalisasi Kinerja Klasifikasi Melalui Seleksi Fitur dan AdaBoost dalam Penanganan Ketidakseimbangan Kelas

One of the problems in data mining classification is class imbalance, where the number of instances in the majority class is more than the minority class. In the classification process, minority classes are often misclassified, because machine learning prioritizes the majority class and ignores the minority class so that this can cause the classification performance to be not optimal. The purpose of this study is to provide a solution to overcome class imbalances so as to optimize classification performance using chi-square and adaboost on one of the classification algorithms, namely C5.0. In this study, the majority class in the dataset used is dominated by the negative class, so the performance appraisal should focus more on the positive class. Therefore, a more suitable assessment is recall/sensitivity/TPR because the resulting value only depends on the positive class. The results showed that both methods were able to increase the recall/sensitivity/TPR value, meaning that the application of chi-square and adaboost was able to improve the classification performance of the minority class
Bridging Global Context Interactions for High-Fidelity Image Completion

Bridging global context interactions correctly is important for high-fidelity image completion with large masks. Previous methods attempting this via deep or large receptive field (RF) convolutions cannot escape from the dominance of nearby interactions, which may be inferior. In this paper, we propose to treat image completion as a directionless sequence-to-sequence prediction task, and deploy a transformer to directly capture long-range dependence in the encoder. Crucially, we employ a restrictive CNN with small and non-overlapping RF for weighted token representation, which allows the transformer to explicitly model the long-range visible context relations with equal importance in all layers, without implicitly confounding neighboring tokens when larger RFs are used. To improve appearance consistency between visible and generated regions, a novel attention-aware layer (AAL) is introduced to better exploit distantly related high-frequency features. Overall, extensive experiments demonstrate superior performance compared to state-of-the-art methods on several datasets.
Improving traffic sign recognition by active search

We describe an iterative active-learning algorithm to recognise rare traffic signs. A standard ResNet is trained on a training set containing only a single sample of the rare class. We demonstrate that by sorting the samples of a large, unlabeled set by the estimated probability of belonging to the rare class, we can efficiently identify samples from the rare class. This works despite the fact that this estimated probability is usually quite low. A reliable active-learning loop is obtained by labeling these candidate samples, including them in the training set, and iterating the procedure. Further, we show that we get similar results starting from a single synthetic sample. Our results are important as they indicate a straightforward way of improving traffic-sign recognition for automated driving systems. In addition, they show that we can make use of the information hidden in low confidence outputs, which is usually ignored.
ORGANIZE LEADER LEARNING ACTIVITIES OF STUDENTS SEMINAR CLASSES

In the article, seminars are an active form of independent work of students. It is in these lessons that the full realization of the “teacher – student” relationship can be achieved, students can consciously master the material studied, get answers to incomprehensible questions. In a number of cases, there is a discussion in the classroom, in which students argue and convinceothers of the ideas put forward, stating that students need to expand their knowledge.
Application of K-Means Algorithm in Grouping Households Accessing the Internet by Province

The study aims to group households that access the Internet according to the province. As for the data source in this study was made from BPS (the statistical center body) and the data used in the study was in 2017 to 2019 isolated from 34 provinces in Indonesia. The method of artificially synthesizing the research is using a k-means algorithm. According to the data, groups of households that access the Internet according to the provinces are grouped into 2 clusters of high clusters (c1) and low cluster (c2). It is hoped that this study will provide more attention to the government for the provinces that have low Internet access, It could also lead to programs that would seek to improve people's access to the Internet via e-government, telencenter, smart villages, or smart city, and indonesian-to pursue their relationship with more advanced Internet countries such as Europe and America
Human-Like Computational Reasoning: Diagrams and Other Representations

In this chapter, I give a personal account of my experience in Alan Bundy’s DReaM group in the Department of Artificial Intelligence at the University of Edinburgh between the years of 1995 and 1998. Of course, the impact of this experience has been profound and long-lasting to this day. The culture and the nature of research work, the collaborations, the interests and the connections have endured, evolved and multiplied throughout this time. My own work in the DReaM group started by investigating human “informal” reasoning and formalising it in a diagrammatic theorem prover. After leaving Edinburgh, this work naturally evolved into combining diagrams with other representations in a uniform framework, as well as applying visual representations in other domains, such as reasoning with ontologies. But one of the fundamental questions remained unanswered, namely, how do we choose the right representation of a problem and for a particular user in the first place?
Internet of Things Based Emotional Sensor System to Predict the Patient’s Information Using Bioscience Technology

Today we are living in the modern machine world, which leads gradual increase of diseases in the real human world.The number of person who works with computer or electronic devices are more in the recent decades.Due to the usage of electronic device, people are affected mentally rather than physical.Determine the emotion of the people while working with computers or any electronic devices termed as blue eye technology.This technology used to predict the emotion of the user such as joy, sad, happy or disgust.This paper mainly focused on remote monitoring system of the patient information using Blue Eye technology with IoT.Physicians can directly monitor the patient emotional information using Emotional Sensor system in terms of blue eye technology which act as an important fact of bio science.The Analog data retrieved form the emotional sensor are transmitted to cloud database in the digital form.These digital data will be predicted with K-NN algorithm to determine the patient's information.This paper concluded that an emotional sensor system used to predict the patient's information in the bioscience technology would be effective.This proposed work monitor both the emotional information and also physical information in terms of heart beat, pressure etc.This proposed work will provide an effective and efficient result in monitoring the patient information using bio science with Iot.
INTRODUCTION OF INNOVATIVE TECHNOLOGIES TO THE SYSTEM OF CONTINUOUS EDUCATION OF PRIMARY SCHOOL TEACHERS IN THE CONTEXT OF EDUCATIONAL REFORM

The article presents the results of a study conducted by the Department of Primary Education of the Donetsk In-Service Teacher Training Institute on solving the problem of forming the readiness of primary school teachers to work in the New Ukrainian School by means of innovative technologies. To achieve this goal the theoretical and practical research methods were used. The final indicators of the assessment of the level of readiness of primary school teachers to work in the New Ukrainian School on the basis of introduction the innovations indicate the effectiveness of the selected innovative instruments of continuing teachers’ education.
Creating an Open-Source Augmented Reality Remote Support Tool for Industry: Challenges and Learnings.

Abstract Remote support tools offer great savings potential for maintenance work and simplify communication between on-site workers and remote experts in the back-office. Despite the predicted benefits and the existence of an increasing number of commercial augmented reality (AR) remote support tools, the technology still struggles with various challenges, mainly caused by technical limitations, safety concerns or industry-specific constraints. In this paper, we describe the implementation of an open source AR remote support application with a strong industry focus. The software was developed based on the feedback of four industry partners with different needs in the field of maintenance work. Therefore, we set the focus on configurability to provide the right tools for each use case. The developed remote support tool was then compared with traditional support measures in a user study. This paper presents the lessons learned from the expert discussions with our company partners and the results of the user study.
A category theory framework for Bayesian learning

Inspired by the foundational works by Spivak and Fong and Cruttwell et al., we introduce a categorical framework to formalize Bayesian inference and learning. The two key ideas at play here are the notions of Bayesian inversions and the functor GL as constructed by Cruttwell et al.. In this context, we find that Bayesian learning is the simplest case of the learning paradigm. We then obtain categorical formulations of batch and sequential Bayes updates while also verifying that the two coincide in a specific example.
How Well Do Sparse Imagenet Models Transfer?

Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" specialized datasets. Generally, more accurate models on the "upstream" dataset tend to provide better transfer accuracy "downstream". In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have been pruned - that is, compressed by sparsifying their connections. We consider transfer using unstructured pruned models obtained by applying several state-of-the-art pruning methods, including magnitude-based, second-order, re-growth, lottery-ticket, and regularization approaches, in the context of twelve standard transfer tasks. In a nutshell, our study shows that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities, and, while doing so, can lead to significant inference and even training speedups. At the same time, we observe and analyze significant differences in the behaviour of different pruning methods.