Reviewerly/exHarmony huggingface dataset:
Application of Augmented Reality for Recurrent Laryngeal Nerve Identification During Robotic Thyroid Surgery

Introduction: Augmented reality (AR) allows the user to see the real world, with virtual objects superimposed upon or composited with the real world.1 Incorporation of AR in laparoscopic or robotic surgery aids the surgeon to more easily perceive anatomical structures intuitively, which helps to identify structures covered or hidden by surrounding tissue.2 Recurrent laryngeal nerve (RLN) injury is one of the most serious complications after thyroid surgery, which results in vocal cord palsy.3 Compared with the open thyroid surgery, identifying the RLN in the robotic thyroid surgery is more challenging because surgeons cannot use the tactile sense. In this study, we adopted AR to help surgeons to localize the RLN during the robotic thyroid surgery. Materials and Methods: AR was applied in real time for nine patients who underwent robotic thyroidectomy because of papillary thyroid carcinoma, and bilateral axillo-breast approach robotic thyroidectomy was performed. We constructed the AR images of the common carotid artery and the trachea using the computed tomography images. Based on the DICOM files from patients, AR images of the structures of interest segmented, and then 3D volume rendering was performed through segmented section images using open source software Seg3D (v 2.4.3).4 Before overlay, we applied simultaneous localization and mapping technique,5 which enables surgical map construction and vision-based tracking using MAXST AR SDK (v 4.1.4). The AR image of the RLN was positioned to the side of the trachea with 8 mm distance, which was determined in the pilot study. The actual trachea and right common carotid artery were overlaid with AR images of the trachea and right common artery using a mouse controller. Results: AR was applied in real time for two patients who underwent robotic thyroidectomy because of thyroid cancer. The patient had no vocal cord paralysis on the postoperative laryngoscopic examination. Conclusions: In this prospective study, AR was effectively applied to robotic thyroid surgery. AR that overlaps on the actual organs may be helpful for surgeons to identify hidden anatomical structures during the robotic surgery. No competing financial interests exist. Runtime of video: 5 mins 17 secs
A Task-Aware Network for Multi-task Learning

Creating a model capable of learning new tasks without deteriorating its performance on the previously learned tasks has been a challenge of multi-task learning. Fine-tuning a pre-trained network for another task could change the network in a way that degrades the performance on its original task. In this paper, we proposed a novel deep network for learning multiple tasks based on extendable Dynamic Convolutional Blocks. Using the dynamic residual connections between layers, our method adjusts the network depth to enable adaptability. The activation function selection strategy accommodates a variety of choices in the training of the network for a specific task. We evaluated our method using a publicly available dataset ALFW and conduct a comparison study against the state-of-the-art methods. It was demonstrated that our multi-task network outperforms the existing single and multi-task methods by reducing the average error by as much as 25%. Our method also exhibits a greater consistency for different tasks. By training variations of our proposed MTN, we observed that MTN-3 achieved the best performance with a cumulative error rate of 1.87% and 5.7% reduction in average error.
Technologies of Artistic Image Perception in Educational Interaction

Sherban T. Technologies of Artistic Image Perception in Educational Interaction / Tetуana Sherban, Volodymyr Hoblyk, Tamara Bondar, Anna Shcherban, Innocent Korniienko, Nadiia Reznik / Interaction. International Journal of Advanced Science and Technology . - 2020. - Vol. 29 No. 9s. - Р. 4574 - 4581
Personalization of learning using adaptive technologies and augmented reality.

The research is aimed at developing the recommendations for educators on using adaptive technologies and augmented reality in personalized learning implementation. The latest educational technologies related to learning personalization and the adaptation of its content to the individual needs of students and group work are considered. The current state of research is described, the trends of development are determined. Due to a detailed analysis of scientific works, a retrospective of the development of adaptive and, in particular, cloud-oriented systems is shown. The preconditions of their appearance and development, the main scientific ideas that contributed to this are analyzed. The analysis showed that the scientists point to four possible types of semantic interaction of augmented reality and adaptive technologies. The adaptive cloud-based educational systems design is considered as the promising trend of research. It was determined that adaptability can be manifested in one or a combination of several aspects: content, evaluation and consistency. The cloud technology is taken as a platform for integrating adaptive learning with augmented reality as the effective modern tools to personalize learning. The prospects of the adaptive cloud-based systems design in the context of teachers training are evaluated. The essence and place of assistive technologies in adaptive learning systems design are defined. It is shown that augmented reality can be successfully applied in inclusive education. The ways of combining adaptive systems and augmented reality tools to support the process of teachers training are considered. The recommendations on the use of adaptive cloud-based systems in teacher education are given.
MultiVitaminBooster at PARSEME Shared Task 2020: Combining Window- and Dependency-Based Features with Multilingual Contextualised Word Embeddings for VMWE Detection

In this paper, we present MultiVitaminBooster, a system implemented for the PARSEME shared task on semi-supervised identification of verbal multiword expressions - edition 1.2. For our approach, we interpret detecting verbal multiword expressions as a token classification task aiming to decide whether a token is part of a verbal multiword expression or not. For this purpose, we train gradient boosting-based models. We encode tokens as feature vectors combining multilingual contextualized word embeddings provided by the XLM-RoBERTa language model with a more traditional linguistic feature set relying on context windows and dependency relations. Our system was ranked 7th in the official open track ranking of the shared task evaluations with an encoding-related bug distorting the results. For this reason we carry out further unofficial evaluations. Unofficial versions of our systems would have achieved higher ranks.
Vital Geometry by P. Sloterdijk: Spherological Approach to Humans Understanding

The study represents the author’s interpretation of the concept by P. Sloterdijk. The spherological ap-proach allows us to expand the boundaries of hu-man understanding through the convergence of psychology, phenomenology and philosophical anthropology. The origins of spherology as an ele-ment of vital geometry could be found in Antique Philosophy and is further represented in culture reflecting the fundamental aspects of human exist-ence. P. Sloterdijk’s spherological concept allows us to explore the dimension of internal spatiality of a person reconstructing the concept of intimacy. Mi-crospherology and macrospherology are becoming modes of human existence, and spherological to-pology takes place as a space of intimacy solely. The phenomenon of sphere-formation is considered by P. Sloterdijk through the prism of extrapolation of the natural to the artificial. In fact, the need to recre-ate a comfortable protective space is a way of self-preservation, which is immanently inherent in a per-son. The process of sphere-formation is a recon-struction of the first spherological defense obtained from pre-personal dyadic experience. Since spher-ological spaces are constituted by subjects, the philosophical question about man within the framework of spherology is the decentralization of the individual and the recultivation of implicit dyadic connections. The spherological approach allows us to re-evaluate the formation of society history, the development of art and the morphology of human thinking.
A prototype of dextran content record system in sugar and alcohol production using blockchain technology.

ABSTRACT. Dextran is a polysaccharide produced by bacteria of the genus Leuconostoc spp, which causes loss of sucrose and raises sugar and alcohol manufacturing costs, as well as reducing its quality. Therefore, it is important to control the proliferation of this bacterium, both in the sugarcane field and in agroindustry. In the field should be observed, the length of stay between cutting and transport to industry, the presence of impurities, among other factors. In industry, long storage periods should be avoided, as well as taking care of cleaning and asepsis of the mills and broth treatment tanks. The objective of this work is to record dextran content using blockchain technology because it is a distributed database suitable for applications such as this, which will track the dextran content from the sugarcane field to the production of sugar and alcohol in the industry. A blockchain database is very secure against tampering, which is another advantage of this solution.
Effectiveness of AI Editing Software for Corrective Feedback in L2 Writing

This study examined whether AI-based editing software can contribute to the improvement of L2 learners’ writing quality measured by four indicators: syntactic complexity, lexical variety, lexical richness, and readability. A total of 65 college students at a low-intermediate level of fluency participated in this study. One class, consisting of 30 students, was randomly designated as the control group, and another class with 35 students as the experimental group. Free versions of Grammarly, an AI-based written corrective feedback provider, and Quillbot, an AI-based paraphrasing suggestion provider, were used as a treatment for the experimental group. The analysis revealed that the experimental group outperformed the control group in terms of syntactic complexity, lexical variety, and readability. However, little difference has been detected between the groups in terms of lexical richness. This result suggests potential pedagogical capabilities AI-based editing software can offer L2 writers and teachers.
APPLICATION OF INNOVATIVE METHODS IN SCHOOL ACTIVITIES ACCORDING TO TEACHERS

The report analyzes the results of an empirical study conducted in 2019 with 562 teachers from different types of schools, settlements in Bulgaria and stages of education. Their views and attitude to the application of innovative methods in school activities – curricular, extracurricular and others were monitored. Based on the collected data, a profile of the modern teacher as a school innovator was outlined. The report include information about: what forms, methods and tools teachers use in their practice; what innovative methods they know, how often apply them and what motivated them; whether the innovative methods help in the student’s learning process; the extent to which innovative teaching methods increase the quality of learning content. A special focus in the study was placed on the role of teacher’s education and proffesional qualification for the application of innovative methods, as well as school management for the introduction of innovations and support to teachers in this process.
Application and comparison of different classification methods based on symptom analysis with traditional classification technique for breast cancer diagnosis

Novel approach for classification technique such as Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA) and Random Forest (RF) using factor or dichotomic variables has been introduced. This study searches for the highly informative finitely linear combinations (symptoms) of variables in the finite field on the based of the Fisher’s exact test and accurately predict the target class for each case in the data. There are several super symptoms have comparable p-values. In this case, it becomes possible to choose as a nominative representative the factor which is more accessible for interpretation. The super symptom means a linear combination of various multiplications of k dichotomous variables over a field of characteristic 2 without repeating. In algebra, such functions are called Zhegalkin polynomials or algebraic normal forms.
ADAPT at SR’20: How Preprocessing and Data Augmentation Help to Improve Surface Realization

In this paper, we describe the ADAPT submission to the Surface Realization Shared Task 2020. We present a neural-based system trained on the English Web Treebank and an augmented dataset, automatically created from existing text corpora.
Pengembangan Lembar Kerja Peserta Didik (LKPD) Pembelajaran Matematika Berbasis Pendekatan Contextual Teaching And Learning (CTL) Pada Materi Bilangan Bulat di Kelas V SD

Abstract: This research generally aims to produce a Learner Worksheet (LKPD) based on a contextual teaching and learning (CTL) approach that is feasible, practical, and effective. This research is a type of development research (Rd b) Practical, with an average percentage of teacher and student response questionnaires of 86.6% ; c) Effective, with an average evaluation test result percentage of 83.5. Based on these results, The Student Worksheet (LKPD) based on contextual teaching and learning (CTL) approach can be used in classroom learning to improve students' learning motivation so that learning can run more effectively.
Using Facebook Social Media Amidst COVID-19 as an E-Learning Platform

Facebook Social Media today plays a vital role in our society, especially during the pandemic season Most of us use Facebook as an alternative tool for communication and also served as the e-Learning Platform After the declaration of suspension of classes last March 16, 2020, there is no way to hold face to face class instruction or rather the traditional classroom, good thing is that I have started creating a Facebook group chat with all my classes at the college level and graduate school level Moreover, The study can be used to improve the use of Facebook social media tool like for teaching and learning purposes This is the study which analyzed Facebook implementation for academic purposes by using a linear and nonlinear modelling
Role of Spy-ware Analysis in the Arena of Cybersecurity

This research aims to investigate the seriousness of the Cybersecurity for spyware including various types of spyware and the ways to enhance the security for reducing the spyware threats.
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning

We propose a simple class of deep reinforcement learning (RL) methods, called FactoredRL, that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms. In tabular and linear approximation settings, the factored Markov decision process literature has shown exponential improvements in sample efficiency by leveraging factored environment structures. We extend this to deep RL algorithms that use neural networks. For model-based algorithms, we use the factored structure to inform the state transition network architecture and for model-free algorithms we use the factored structure to inform the Q network or the policy network architecture. We demonstrate that doing this significantly improves sample efficiency in both discrete and continuous state-action space settings.
Comparison of Swarm Optimization and Memetic Algorithm for Systolic Mapping of Texture Analysis

Systolic processors offer a hardware design which can accommodate more functions in a small footprint. Hardware utilization efficiency can be enhanced by appropriately designating the intended hardware with a task in space and time through parallel computing platforms. Regular algorithms known for their computational complexity can be mapped to systolic array by dependence graphs, which allot hardware to the design data. Manual mapping techniques tend to be tedious with more inaccuracy and calls for efficient mapping techniques, automated through algorithmic procedures. Texture Analysis marks the preliminary progression of image analysis and interpretation. Automotive systems, Robotics, Industrial processing and similar automated applications can be simplified through texture analysis. This work deals with employing evolutionary algorithms for mapping texture analysis onto systolic architecture. Memetic Algorithms (MA) and Particle Swarm Optimization (PSO) algorithms were comparatively studied and the efficiency of designing a parallel architecture through systolic array is analyzed through cost function and processing time.
BSU TALKnology: Touching Lives of Rural Communities in the Highlands of Northern Luzon, Philippines

The TALKnology program, a 15-minute radio program of the  Benguet State University, is seen as an effective modality in the  adoption of technologies among rural households in the highland  communities of Northern Luzon, Philippines. To assess the  efficacy of this program in bringing out social outcomes geared  towards empowering farmers, there is a need to gather baseline  information, hence this study. The study characterized the socio- economic status of the respondents; determined their attitude on the topics discussed in the program; categorized the technologies  adopted by the respondents as influenced by the program;  determined the positive results attained by the rural households  after the adoption of the technologies, and identified the challenges  encountered. Results showed that the TALKnology program  contributed to the enhanced awareness of rural communities  about appropriate technologies. In turn, the enhanced awareness  has brought desirable changes among rural communities such as the adoption of BSU technologies that are mostly along agriculture.  Such technologies include compost making, basic plant and animal  pest and disease management, soil sampling and soil analysis,  egg production, and postharvest technologies. Adopting the  technologies accessed through the TALKnology program enhanced  the livelihood opportunities of the rural households. Limited  financial resources, however, hindered the adoption of some  technologies.
Mean Square Error Performance of the Modified Jackknifed Ridge Predictors in the Linear Mixed Models

The goal of this study is to introduce the modified jackknifed ridge prediction method in the linear mixed models. Then, the matrix mean square error (MMSE) comparisons are done. Finally, a real data analysis is given to observe the behavior of the modified jackknifed ridge predictors.
Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality

As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, where authors are labelled with personality traits. The traits are derived from one of the mainstream personality theories in psychology research, named MBTI. Using personality prediction experiments, we (i) study the task of personality prediction in itself on our corpus as well as on TWISTY, a Twitter dataset also annotated with MBTI labels; (ii) carry out an extensive, in-depth analysis of the features used by the classifier, and view them specifically under the light of the original theory that we used to create the corpus in the first place. We observe that no single model is best at personality detection, and that while some traits are easier than others to detect, and also to match back to theory, for other, less frequent traits the picture is much more blurred.
Human Spiritual Being As A Complex Biological And Psychosocial Phenomenon

The article synergistically analyzes biosocial, psycho-spiritual phenomena that play a crucial role in human existence. And also, the formation, existence and interaction of the biological, social and spiritual existence of man as a system are philosophically analyzed. Particular attention is paid to the disclosure of the synergistic nature of the phenomena of thinking, creativity and freedom.
A Graph Embedding Technique for Weighted Graphs Based on LSTM Autoencoders

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.
DESAIN PEMBELAJARAN DISASSURE BERBASIS KETERAMPILAN ABAD 21 DALAM PEMBELAJARAN BAHASA JEPANG

This research is motivated by the need for the ability to master 21st century skills in line with the changing times. Therefore, language learning is needed for an instructional model that adjusts to the 21st century skills. An effective and fun learning design is needed in learning Japanese to make learners more active and motivated in learning the language. This research is a literature study. The purpose of this study was to describe how to illustrate and apply the Discovery Learning and ASSURE learning models by adding 21st century skills to Japanese grammar learning.The method was used was qualitative approach. This type of research is descriptive research. This study aims to describe an instructional design model for the development of a learning model that modifies Discovery Learning and ASSURE with the addition of 21st century skills, such as critical thinking, collaboration and communication. This research finding are the student centered instruction model that adjust with the 21st century skills, and the teacher as facilitators encourage learners to be responsible for the learning process they are doing so that learners can find the concept of their own sentence patterns, practice critical thinking , communication and collaboration skills.
Document-Level Neural Machine Translation Using BERT as Context Encoder.

Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. The methods of incorporating BERT into document-level machine translation are still being explored. BERT is able to understand sentence relationships since BERT is pre-trained using the next sentence prediction task. In our work, we leverage this property to improve document-level machine translation. In our proposed model, BERT performs as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. Experiment results show that our proposed method can significantly outperform strong document-level machine translation baselines on BLEU score. Moreover, the ablation study shows our method can capture document-level context information to boost translation performance.