Reviewerly/exHarmony huggingface dataset:
피부미용 졸업생의 NCS 직업기초능력과 하위능력에 대한 산업체의 교육요구도 분석

The importance of flexibility and adaptability in the global knowledge-based economy is a primary concern in current education and training. The present work intended to investigate the current status and needs of the basic vocational competencies required from the graduates of G college. The study conducted surveys in aesthetic industries where the graduates are employed. The data were analyzed using Borich needs analysis and the Locus of Focus Model. According to redundancy analysis between top priority items in the formula for the priority decision of Borich and HH quadrant items in the Locus for Focus Model, the highest priority group comprised ‘customer service competency’, ‘language proficiency competency’, ‘basic foreign language competency’ and ‘negotiation competency’, while the next priority group included ‘teamwork competency’ and ‘conflict management competency’. The research results showed that basic vocational competencies need to be developed in the areas of ‘communication competency’, ‘interpersonal competency’ and ‘information competency’. Based on the results of this study, several tasks to improve the basic vocational competencies were suggested for education in colleges.
Augmented Reality and Gamification: A Framework for Developing Supplementary Learning Tool

Purpose – The study’s main purpose is to develop a supplementary learning tool framework by the use of a dynamic mobile application using Unity AR and Vuforia for Senior High School (SHS) students and teachers to help the learning process in SHS Earth Science.Method – The researchers will be using the Software Development Life Cycle (SDLC) Model of methodology to ensure the quality of the software as well as the correctness of the development process.Results – The expected result of the study is that Augmented Reality and Gamification will now be used as a supplementary learning tool in SHS Earth Science.Conclusion – Augmented Reality and Gamification can now be used as a supplementary learning tool in SHS Earth Science using the designed framework.Recommendations – Future studies will focus on the development of the framework and the mobile application.Research Implications – Since the system has a lot of potential in the education sector and due to the effects of COVID-19, the software will serve as a pioneer to show that a supplementary tool will help students learn logically and entertainingly especially since schools nowadays are transitioning with either distance learning or blended learning.
IIT_kgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports.

The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.
TRAVIS at PARSEME Shared Task 2020: How good is (m)BERT at seeing the unseen?

This paper describes the TRAVIS system built for the PARSEME Shared Task 2020 on semi-supervised identification of verbal multiword expressions. TRAVIS is a fully feature-independent model, relying only on the contextual embeddings. We have participated with two variants of TRAVIS, TRAVIS-multi and TRAVIS-mono, where the former employs multilingual contextual embeddings and the latter uses monolingual ones. Our systems are ranked second and third among seven submissions in the open track, respectively. Despite the strong performance of multilingual contextual embeddings across all languages, the results show that language-specific contextual embeddings have better generalization capabilities.
Chat Bot Application Using Artificial Intelligence

Chat bots can have functionality which ranges from little to high which is a virtual conversation but still have a huge impact with great brand message, which is perfect for handling various aspect which involves human intervention especially for marketing campaigns. The way which ideas are shared between two humans is through conversation, therefore Chabot helps in By definition we can understand that, a chat bot is a program responds to the given input either in speech recognition or textual manner which is understood by a human. The main advantage of chatbot is it decreases the repetitive task done by human with same questions. Its main advantage is the questions asked which in turn improves customer experience that maintain uniqueness, scalability and sends automated responses. Currently chatbots can be classified depending on the kind of model used to give better engagement of two brands by having1-on-1 communication at a standard level. Brands have the opportunity to address the issue involved with better assistance.
LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea

Developing a text readability assessment model specifically for texts in a foreign English Language Training (ELT) curriculum has never had much attention in the field of Natural Language Processing. Hence, most developed models show extremely low accuracy for L2 English texts, up to the point where not many even serve as a fair comparison. In this paper, we investigate a text readability assessment model for L2 English learners in Korea. In accordance, we improve and expand the Text Corpus of the Korean ELT curriculum (CoKEC-text). Each text is labeled with its target grade level. We train our model with CoKEC-text and significantly improve the accuracy of readability assessment for texts in the Korean ELT curriculum.
Towards Practical Second Order Optimization for Deep Learning

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs. In an attempt to bridge this gap between theoretical and practical optimization, we present a scalable implementation of a second-order preconditioned method (concretely, a variant of full-matrix Adagrad), that along with several critical algorithmic and numerical improvements, provides significant convergence and wall-clock time improvements compared to conventional first-order methods on state-of-the-art deep models. Our novel design effectively utilizes the prevalent heterogeneous hardware architecture for training deep models, consisting of a multicore CPU coupled with multiple accelerator units. We demonstrate superior performance compared to state-of-the-art on very large learning tasks such as machine translation with Transformers, language modeling with BERT, click-through rate prediction on Criteo, and image classification on ImageNet with ResNet-50.
대학생의 실용영어 수업에 적용한 학생 질문만들기 활동의 효과

The purpose of this study is to look into the effects of student-generated questions on college students’ academic achievement and academic efficacy. For this study, thirty college students were chosen as the participants. They were asked to generate questions through their e-campus learning management system every other week and to do group-generated question activities in class over 16 weeks. The data was collected from pre-and post-TOEIC tests, and academic efficacy questionnaires. The following results have been obtained. First, this study showed that there was significant improvement in students’ academic achievement based on the TOEIC scores. Second, there were meaningful changes in academic efficacy as shown in the questionnaires. Student-generated questions have played a great role in raising students’ self-confidence, task difficulty preferences and self-regulatory efficacy toward academy. Several suggestions for follow-up studies are presented based on the findings of this study.
Forensic Analysis of Peer-to-Peer Network Traffic with Wireshark

The continuous rise of network security threats and network attacks have motivated accelerated studies on network forensics. Typically, data collected in a networked system is often used to investigate security threats. One of the principles and techniques of network security is packet analysis, which is a branch of network security that studies insecure protocols. In order to help with the forensic investigation and facilitate the fight against cybercrimes, we carried out a live investigation of network packets on a BitTorrent client. This paper proposes a capture and analysis technique for network packets using Wireshark. Network traffic of P2P networks was monitored, captured and analyzed. The results of the analysis showed that the proposed technique was able to successfully identify the source and location of threats on the network that can be verifiable as a credible digital evidence for forensic investigations.
Counterfactual Thinking for Long-tailed Information Extraction

Information Extraction (IE) aims to extract structured information from unstructured texts. However, in practice, the long-tailed and imbalanced data may lead to severe bias issues for deep learning models, due to very few training instances available for the tail classes. Existing works are mainly from computer vision society, leveraging re-balancing, decoupling, transfer learning and causal inference to address this problem on image classification and scene graph generation. However, these approaches may not achieve good performance on textual data, which involves complex language structures that have been proven crucial for the IE tasks. To this end, we propose a novel framework (named CFIE) based on language structure and causal reasoning with three key ingredients. First, by fusing the syntax information to various structured causal models for mainstream IE tasks including relation extraction (RE), named entity recognition (NER), and event detection (ED), our approach is able to learn the direct effect for classification from an imbalanced dataset. Second, counterfactuals are generated based on an explicit language structure to better calculate the direct effect during the inference stage. Third, we propose a flexible debiasing approach for more robust prediction during the inference stage. Experimental results on three IE tasks across five public datasets show that our model significantly outperforms the state-of-the-art models by a large margin in terms of Mean Recall and Macro F1, achieving a relative 30% improvement in Mean Recall for 7 tail classes on the ACE2005 dataset. We also discuss some interesting findings based on our observations.
Personality-Oriented Model in the Preparation of an Engineer-Teacher

The scope and content of the profession "an engineer with professional qualification for teaching" should be considered as a complex combination of engineering, psycho-pedagogical and methodological components. The ability to absorb them in a quality way will enable the individual, to a certain extent to perform the functions assigned to him in a more comprehensive manner. The question "What is the pedagogical essence of the profession" engineer-teacher?” is extremely important in that relation. Long-term experience in the preparation of such specialists allowed the the formation of a specific Person-oriented model that reveals the pedagogical essence of this process. In current articles presents the structure and its development.
Patents for NLP Software: An Empirical Review

Natural language processing (NLP) patents are one of the fastest growing niche segments in the technology patent marketplace. NLP technologies power the latest in Artificial Intelligence (AI) applications. For example, NLP technology supports Apple’s Siri, Amazon’s Alexa, and Facebook’s friend recommendation system. Yet, while the literature on software patents is visibly scaling – the literature specifically focused on NLP patents is non-existent. This Article draws on a growing body of computational linguistics, intellectual property, and technology law scholarship to provide novel NLP patent analysis and critique. Further, this Article contributes the first empirical NLP patent review, including novel software descriptions, market modeling, and legal analysis relating to patent claims. First, this Article discusses the two main technical approaches to developing NLP software. Second, this Article models an evolving NLP patent dataset, offering economic insights, legal claims analysis, and patent valuation strategies.
International Congress of Philosophy as a Self-Consciousness of Philosophy

The author suggests rethinking the institutional status of philosophy in the con­text of the analysis of International philosophical congresses as collective forms of expression of thought. It is stated that communication platforms for philo­sophical debates such as seminars, conferences, congresses, etc. largely deter­mine the content of philosophical knowledge, therefore it is impossible to raise the question of “pure” philosophy, free from the sphere of conversation (Tatiana G. Shchedrina), in which philosophy is produced as an event. Science and phi­losophy as communicative phenomena require collective forms of work. Scien­tific communities are an indispensable attribute of the existence of science, just like philosophy is the presence of a philosophical school, college, lyceum, acad­emy. Even if science and philosophy are done “armchairly”, then the “armchair” format also presupposes a communication platform, a network of communica­tion between different authors. In this sense, any conferences, congresses, and round tables – with a successful coincidence of academic circumstances – be­come not just a continuation of philosophy but also a direct part. On them, not only and not so much the “philosophical result” is presented, but the process of philosophizing is going on. This indisputable significance of World congresses as collective forms of philosophy and as its self-consciousness was recorded by the famous Russian logician N.A. Vasiliev when he was thinking about the re­sults of the Third World Congress of Philosophy.
A Unified Learning Approach for Hand Gesture Recognition and Fingertip Detection.

In human-computer interaction or sign language interpretation, recognizing hand gestures and detecting fingertips become ubiquitous in computer vision research. In this paper, a unified approach of convolutional neural network for both hand gesture recognition and fingertip detection is introduced. The proposed algorithm uses a single network to predict the probabilities of finger class and positions of fingertips in one forward propagation of the network. Instead of directly regressing the positions of fingertips from the fully connected layer, the ensemble of the position of fingertips is regressed from the fully convolutional network. Subsequently, the ensemble average is taken to regress the final position of fingertips. Since the whole pipeline uses a single network, it is significantly fast in computation. The proposed method results in remarkably less pixel error as compared to that in the direct regression approach and it outperforms the existing fingertip detection approaches including the Heatmap-based framework.
AN ANALYSIS OF IMPLICATURE IN “PEACEFUL WARRIOR” MOVIE SCRIPT

When talking, people often have implied meaning which is actually more significant by content rather than direct meaning in comparison. As a part of linguistic event, implicature is easily found in many occasions. One of them is in a movie dialogue. However, understanding the implied meaning is not everyone‘s skill in and it needs prior knowledge. Therefore, this study aims at (1) finding out the implicature in the Peaceful Warrior movie script and (2) the explanation to the implied meaning used by the actors in the movie. This is a qualitative study by using Yule‘s theory of implicature. The result of this study reveals that there are 20 conversational implicatures found in the movie. Keywords: implicature; implied meaning; peaceful warrior
MARSHAL MCLUHAN’S TECHNOLOGICAL DETERMINISM THEORY IN THE ARENA OF SOCIAL MEDIA

McLuhan (1964) proposed that mediated technologies ensure culture diffusion in a society which in turn helps change human behavior. He states, We shape our tools, and they in turn shape us. Most of the scholars in the field talked about radio and television etc. as mediated technologies but McLuhan perceived a bit differently by including numbers, games and money as mediated. Regarding numbers, he held that every individual in a theater enjoys all those others present. This creates a mass mind which let elites to establish a profile of the crowd. The phenomenon in turn homogenizes the masses that are easily influenced.  McLuhan stated that games are media of interpersonal communication and extension of human social self. Games according to him “allow for people to simultaneously participate in an activity that is fun and that reflect who they are.” To him money is power that facilitates access. It is money that empowers people to travel the world and serve as transmitter of information, knowledge and culture. These mediated tools according to McLuhan turn the world into a global village. The media of social networking exactly play the same role attributed by McLuhan with number, game, money along with traditional media of radio and television.  Social Media (SM) users make use of identical applications and undertake almost similar activities that turn them into a homogeneous mass. Likewise, online profiles reflect identity features and most of them consume SM to have fun. SM connects people across cultures, religions, and boundaries and let them feel members of a single community. SM has not only converted the world into a small village but also shaped every aspect of human social life.
Training with adversaries to improve faithfulness of attention in neural machine translation

Can we trust that the attention heatmaps produced by a neural machine translation (NMT) model reflect its true internal reasoning? We isolate and examine in detail the notion of faithfulness in NMT models. We provide a measure of faithfulness for NMT based on a variety of stress tests where model parameters are perturbed and measuring faithfulness based on how often the model output changes. We show that our proposed faithfulness measure for NMT models can be improved using a novel differentiable objective that rewards faithful behaviour by the model through probability divergence. Our experimental results on multiple language pairs show that our objective function is effective in increasing faithfulness and can lead to a useful analysis of NMT model behaviour and more trustworthy attention heatmaps. Our proposed objective improves faithfulness without reducing the translation quality and it also seems to have a useful regularization effect on the NMT model and can even improve translation quality in some cases.
Studi Etnografi Pelaksanaaan Gerakan Literasi Sekolah (GLS) Dalam Keterampilan Berbahasa di Kelas V SDN Model Kota Malang

This study aims to determine the implementation of the School Literacy Movement (GLS) in Language Skills in Class V SDN Model Kota Malang with an ethnographic research approach. The method used in this research is qualitative with an ethnographic approach that bases research on culture or habits. Based on the results of the research, it can be concluded that SDN Model Kota Malang has implemented GLS in reading activities through reading habits 15 minutes before learning, writing activities are carried out by making summaries and text essays, then literacy in speaking activities by giving a speech or delivering a summary of reading material in front class.
Mask R-CNN for Segmentation of Left Ventricle

Globally, cardiovascular diseases (CVDs) remain the major cause of death among citizens. With echocardiography, doctors are able to diagnose and determine vital parameters for the evaluation of these diseases. Segmentation of left ventricular (LV) from echocardiography is a significant tool for cardiovascular medical analysis. Besides calculating important clinical indices (e.g. ejection fraction), segmentation also can be useful for the investigation of the basic structure of ventricle. Automatic segmentation of the LV has become a valuable means in echocardiography as we can achieve fast and accurate results and a large number of cases can be handled with limited availability of experts. The Convolutional Neural Networks (CNN) have shown outstanding outcomes for image classification, detection, and segmentation in numerous fields. Recently Mask Regions Convolutional Neural Network (Mask R-CNN) has emerged as a very good segmentation model. In this work, Mask R-CNN is proposed for the segmentation of LV. The Mask R-CNN model is first fine-tuned with Common Object in Context (COCO) weights and then the model is trained with our own data. The model first finds out the region of interest (ROI) in the image that contains the desired object i.e. LV. In the ROI, the model segment LV by generating the mask around it. The results demonstrated by the proposed method segments the LV accurately and efficiently with limited training data.
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings.

Fact-based Visual Question Answering (FVQA), a challenging variant of VQA, requires a QA-system to include facts from a diverse knowledge graph (KG) in its reasoning process to produce an answer. Large KGs, especially common-sense KGs, are known to be incomplete, i.e. not all non-existent facts are always incorrect. Therefore, being able to reason over incomplete KGs for QA is a critical requirement in real-world applications that has not been addressed extensively in the literature. We develop a novel QA architecture that allows us to reason over incomplete KGs, something current FVQA state-of-the-art (SOTA) approaches lack.We use KG Embeddings, a technique widely used for KG completion, for the downstream task of FVQA. We also employ a new image representation technique we call Image-as-Knowledge to enable this capability, alongside a simple one-step co-Attention mechanism to attend to text and image during QA. Our FVQA architecture is faster during inference time, being O(m), as opposed to existing FVQA SOTA methods which are O(N logN), where m is number of vertices, N is number of edges (which is O(m^2)). We observe that our architecture performs comparably in the standard answer-retrieval baseline with existing methods; while for missing-edge reasoning, our KG representation outperforms the SOTA representation by 25%, and image representation outperforms the SOTA representation by 2.6%.
Let’s get into it

Abstract This squib briefly explores how contextualized embeddings – which are a type of compressed token-based semantic vectors – can be used as semantic retrieval and annotation tools for corpus-based research into constructions. Focusing on embeddings created by the Bidirectional Encoder Representations from Transformer model, also known as ‘BERT’, this squib demonstrates how contextualized embeddings can help counter two types of retrieval inefficiency scenarios that may arise with purely form-based corpus queries. In the first scenario, the formal query yields a large number of hits, which contain a reasonable number of relevant examples that can be labeled and used as input for a sense disambiguation classifier. In the second scenario, the contextualized embeddings of exemplary tokens are used to retrieve more relevant examples in a large, unlabeled dataset. As a case study, this squib focuses on the into - interest construction (e.g. I’m so into you ).