Reviewerly/exHarmony huggingface dataset:
외식전공 대학생의 창의성 능력과 기업가 정신, 자기효능감, 진로준비행동 간의 구조적 관계

The purpose of this study is to identify the structural relationship between creative ability, entrepreneurship, self-efficacy, and career preparation behavior in food & culinary arts students. For collecting the data, this study distributed 400 questionnaires to food service majors from March 24, 2019 to March 31, 2019. Total of 400 questionnaires were returned and the researcher removed 79 questionnaires that could not be used statistically (same answers, omission of questions/answers). As a result, total of 322 questionnaires were used for an empirical analysis. In this study, SPSS 18.0 Package and AMOS 18.0 were used for analyzing the statistics. The study results were as follows. First, the result for hypothesis 1 on “Creative ability would have positive (+) influence on entrepreneurship” was adopted. Second, the result for hypothesis 2 on “Creative ability would have positive (+) influence on self-efficacy” was adopted. Third, the result for hypothesis 3 on “Creative ability would have positive (+) influence on career preparation behavior” was rejected. Fourth, the result for hypothesis 4 on “Entrepreneurship would have positive (+) influence on career preparation behavior” was rejected. Fifth, the result for hypothesis 5 on “Self-efficacy would have positive (+) influence on career preparation behavior” was adopted. The results made academic contribution on generalization of social cognitive career theory and career development theory while suggesting practical implications on education and career for food service majors.
High-dimensional Bayesian Optimization for CNN Auto Pruning with Clustering and Rollback.

Pruning has been widely used to slim convolutional neural network (CNN) models to achieve a good trade-off between accuracy and model size so that the pruned models become feasible for power-constrained devices such as mobile phones. This process can be automated to avoid the expensive hand-crafted efforts and to explore a large pruning space automatically so that the high-performance pruning policy can be achieved efficiently. Nowadays, reinforcement learning (RL) and Bayesian optimization (BO)-based auto pruners are widely used due to their solid theoretical foundation, universality, and high compressing quality. However, the RL agent suffers from long training times and high variance of results, while the BO agent is time-consuming for high-dimensional design spaces. In this work, we propose an enhanced BO agent to obtain significant acceleration for auto pruning in high-dimensional design spaces. To achieve this, a novel clustering algorithm is proposed to reduce the dimension of the design space to speedup the searching process. Then, a roll-back algorithm is proposed to recover the high-dimensional design space so that higher pruning accuracy can be obtained. We validate our proposed method on ResNet, MobileNet, and VGG models, and our experiments show that the proposed method significantly improves the accuracy of BO when pruning very deep CNN models. Moreover, our method achieves lower variance and shorter time than the RL-based counterpart.
Hybrid exercising for cyber-resilient healthcare and cross-sector crisis response operability

Achieving a rigorous and resilient cybersecurity posture at a national level nowadays translates to ensuring not only the robustness of critical infrastructures but also that of supporting businesses, academic, and government structures, and their cooperation and readiness to provide a network of support in times of crisis. Approaching resilience maturity is quite the compound subject matter in itself;however, one of the main components on the roadmap to ensuring operational and technical resilience is exercising. Hybrid type of exercises are the intersection between the technical and the managerial-tabletop exercising and are used as a mechanism to maintain a common baseline of readiness for cyber incidents, to increase cross-sector cooperation, and to target specific issues and weaknesses, identified otherwise or from previous exercises. In this paper, we present our approach for the creation of a complex generic cyber range containing a variety of exercise polygons simulating and propagating vertical escalations related to the healthcare sector, its logistics, and supply chains within the context of COVID-19. We further share organizational aspects for the creation of a complex intersection of technical, tabletop, communications, and operational exercise environment and experience. The application of AI/ML methods and tools for exercise facilitation is overviewed. Lessons learned from three types of pilot exercises in critical infrastructures are discussed. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Towards a More Natural Controlled Language in Future Airbus Cockpits. A Psycho-linguistic Evaluation.

The main goal of this research is to optimize an existing Airbus Cockpit Controlled Language in order to integrate it in future cockpit design. The current controlled language used aboard Airbus cockpit interfaces was carefully constructed to avoid ambiguity and complexity. In order to optimize the existing language, we set out to evaluate the appropriate levels of simplification that would achieve more accurate and faster comprehension with optimized pilot training time by using psycho-linguistic experimentation and cognitive science tools. We present in this paper a congruency task similar to traditional judgment tasks in behavioral experiments. It provides a firmly controlled environment to test linguistic hypotheses and CNL rules. Results shows that what we sometimes mistakenly label as superfluous or empty syntactical elements could go a long way in ensuring better comprehension and faster information processing from a psycho-linguistic point of view.
Law School 2061<sup>Ψ</sup>

The Modern Law ReviewEarly View REVIEW ARTICLE Law School 2061Ψ William Lucy, Corresponding Author William Lucy w.n.lucy@durham.ac.uk Law School, Durham University. Thanks to Dino Kritsiotis, John Murphy, Shaun Pattinson and Johanna Jacques, long suffering friends and colleagues, and the reviewers for the MLR, for thoughts and comments. Email: w.n.lucy@durham.ac.ukSearch for more papers by this author William Lucy, Corresponding Author William Lucy w.n.lucy@durham.ac.uk Law School, Durham University. Thanks to Dino Kritsiotis, John Murphy, Shaun Pattinson and Johanna Jacques, long suffering friends and colleagues, and the reviewers for the MLR, for thoughts and comments. Email: w.n.lucy@durham.ac.ukSearch for more papers by this author First published: 09 September 2021 https://doi.org/10.1111/1468-2230.12689 ΨMy title derives from R. Brownsword, ‘In the Year 2061: From Law to Technological Management’ (2015) 7 Law, Innovation and Technology 1. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language Processing

Recent progress in the Natural Language Processing domain has given us several State-of-the-Art (SOTA) pretrained models which can be finetuned for specific tasks. These large models with billions of parameters trained on numerous GPUs/TPUs over weeks are leading in the benchmark leaderboards. In this paper, we discuss the need for a benchmark for cost and time effective smaller models trained on a single GPU. This will enable researchers with resource constraints experiment with novel and innovative ideas on tokenization, pretraining tasks, architecture, fine tuning methods etc. We set up Small-Bench NLP, a benchmark for small efficient neural language models trained on a single GPU. Small-Bench NLP benchmark comprises of eight NLP tasks on the publicly available GLUE datasets and a leaderboard to track the progress of the community. Our ELECTRA-DeBERTa (15M parameters) small model architecture achieves an average score of 81.53 which is comparable to that of BERT-Base's 82.20 (110M parameters). Our models, code and leaderboard are available at https://github.com/smallbenchnlp
Korean Language Education in the Platform Era and Contrastive Linguistics

This paper aims to learn about changes in the paradigm of foreign language education in the era of platform, centering on Korean language education, and to briefly present the contents of Korean language education using Contrastive Linguistics, which learners prefer and can improve learning effects. To this end, we briefly introduce online interaction in the curriculum of foreign language education and some web-based Korean language education services such as 〈Nuri King Sejong Institute〉, 〈Talk to Me in Korean〉, and Mondly, focusing on the level of learner participation. In order to implement learner-directed instruction, it is necessary to develop learning materials tailored to learners, and thus Contrastive Linguistics is expected to play an important role in developing Korean language learning contents using ICT.
Sentiment Analysis in Poems in Misurata Sub-dialect -- A Sentiment Detection in an Arabic Sub-dialect

Over the recent decades, there has been a significant increase and development of resources for Arabic natural language processing. This includes the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic utterances in both Modern Standard Arabic (MSA) and different Arabic dialects. This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool 1. Logistic Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM) classifiers are used with Sklearn, while the Convolutional Neural Network (CNN) is implemented with Mazajak. The results show that the traditional classifiers score a higher level of accuracy as compared to Mazajak which is built on an algorithm that includes deep learning techniques. More research is suggested to analyze Arabic sub-dialect poetry in order to investigate the aspects that contribute to sentiments in these multi-line texts; for example, the use of figurative language such as metaphors.
Implementation of autonomy principle in educational institutions of the Russian Federation

Introduction. Contemporary challenges to the development of education imply the search and understanding of highly effective approaches and management methods. Experts who have conducted PISA studies in Western educational systems have identified a direct correlation between the autonomy of an educational institution and the results of its activities. The extrapolation of PISA results in the Russian education system is possible only with unified theoretical approaches to understanding the conceptual unit of "autonomy", the category "autonomy of an educational institution" and the principle of autonomy, as well as a sociological study, the respondents of which are the heads of educational institutions in Russia. The purpose hereof is a conceptual analysis of existing approaches to understanding the phenomena of "autonomy" and "autonomy of an educational institution" in the legal framework of the Russian education system, in domestic and foreign sources. A certain set of indicators of the phenomenon of autonomy in the Russian educational environment was used in this research as the main toolkit for achieving this goal. Materials and methods. The ideas of the systemic and activity approaches, the general scientific method of comparative analysis serve as the methodological basis of the research. The content-invariant foreign and domestic sources served as the platform for quantitative and qualitative content analysis. In addition to general scientific methods, within the framework of this research, a private scientific survey method was used in the form of a questionnaire survey of the heads of general educational institutions. More than 10 thousand school principals from 84 constituent entities of the Russian Federation took part in the experiment in the course of the sociological research "Autonomy of educational institutions: results and prospects". Research results. The study reveals the theoretical and practical aspects of the implementation of the autonomy principle in the educational system of the Russian Federation. The analysis of domestic and foreign thesaurus on the problem of the autonomy of an educational institution was carried out. The authors dwell in detail on the mechanisms for introducing the principle of autonomy in the educational process in Russia, identify existing obstacles to the implementation of the provisions declared in the legal field. Clustering of elements (indicators) that determine the degree of autonomy is significant for further research of the phenomenon of "autonomy of educational institutions".
Deploy Virtual Machines Using Salt Cloud

As well as sending configurations to your current systems, Salt can be used to manage virtual machines without regard to their location. These virtual machines may be cloud based or on-prem. Imagine this, you need to deploy a virtual machine to test website development. You can easily deploy the system, and once deployed the web server and current content can be delivered reliably with Salt, locally or to the cloud.
On the Inductive Biases of Deep Domain Adaptation

Domain alignment is currently the most prevalent solution to unsupervised domain-adaptation tasks and are often being presented as minimizers of some theoretical upper-bounds on risk in the target domain. However, further works revealed severe inadequacies between theory and practice: we consolidate this analysis and confirm that imposing domain invariance on features is neither necessary nor sufficient to obtain low target risk. We instead argue that successful deep domain adaptation rely largely on hidden inductive biases found in the common practice, such as model pre-training or design of encoder architecture. We perform various ablation experiments on popular benchmarks and our own synthetic transfers to illustrate their role in prototypical situations. To conclude our analysis, we propose to meta-learn parametric inductive biases to solve specific transfers and show their superior performance over handcrafted heuristics.
Self-learning Constellation Mapping Method Based on Neural Networks

Neural networks have been gained extensive study and application in the communication systems. This paper applies the neural networks to the transmitter, which proposes a self-learning constellation mapping method. The proposed method contains two kinds of neural networks. One is used to generate a new constellation mapping which can compensate the channel distortion. The other one is used to determine whether the newly generated constellation mapping is optimal. These two types of neural networks constitute an actor-critic architecture which is trained through the reinforcement learning method. Without losing generality, this paper applies the proposed method to the quadrature amplitude modulation systems. In the simulation experiments, the proposed method can generate some new quadrature amplitude modulation constellation diagrams which can compensate in advance the amplitude and phase distortion caused by the channel. Moreover, the performance of the proposed method is superior to the classical compensation method. Significantly, the neural networks in the proposed method are trained online, so there is no need to prepare a large amount of artificial data sets in the training phase.
금융위기 조기경보모형에서 텍스트 데이터의 유용성에 관한 연구 The Study on the Role of Text Data in the Financial Early Warning System

Korean Abstract: 최근 빅데이터 활용을 위한 텍스트 마이닝 기법이 개발됨에 따라 경제분석에서도 비정형 텍스트 데이터 활용이 확대되고 있다. 본 연구에서는 텍스트 데이터의 유용성을 살펴보기 위해 인터넷 검색 정보를 반영하는 구글 트렌드 데이터를 고려하고 정보의 적시성과 예측력을 필요로 하는 금융위기 조기경보모형에서 텍스트 데이터의 유용성을 분석하였다. 실증분석 결과, 구글 트렌드 데이터는 비모수적 모형이나 모수적 모형에서 모두 금융위기를 예측하는데 유용한 것으로 나타났다. 본 연구 결과는 다양한 텍스트 데이터를 개발하고 경제분석에 활용하는 모형들을 더욱 확대시킬 것을 시사하고 있다.English Abstract: As recently the text mining methodology for the usefulness of the big data has been developed, its’ application in the economic analysis has been growing. This study considers the Google Trends Data which reflects the internet search information in order to examine the usefulness of the text data and applies the data to the financial early warning system which needs the timely information and imminent forecasting. The estimation results show that the Google Trends Data appears to be useful for the prediction of the financial crisis both in the parametric and non-parametric approach. This study suggests that we try to develop more text mining methods and expand their applications to various economic analysis.
An MRC Framework for Semantic Role Labeling

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work. Code is available at \url{https://github.com/ShannonAI/MRC-SRL}.
한국어 논증적 텍스트의 필자 목소리 모형 연구

The purpose of this paper is to construct a auctorial voice model of the Korean argumentative text and verify whether the constructed model fits the characteristics of the Korean argumentative text. To this end, 45 argumentative texts by native Korean speakers were used as research data, and three experts evaluated the appropriateness of the use of auctorial voices. Then, Pearson correlation and linear regression were used to verify whether the overall voice strength and each subcategory had a correlation with the score of the writing texts, and which category of voice had the greatest influence on the score of the writing texts. As a result of the study, it was found that the overall voice strength of the auctorial voice had a correlation with the score of the writing texts. 28.5% of the score was determined by the overall voice strength of the auctorial voice. Among the subcategories of the auctorial voice, the most influential factors on the score of the writing texts were hedges, other’s voice mention, evidence reference, and rebuttal. 66.2% of the overall voice strength was determined by each subcategory of the auctorial voice. rebuttal, interpretation, hedges, and other’s voice mention had the greatest influence on the overall strength of the auctorial voice. Referring to the English auctorial voice model constructed by Zhao(2013), it can be said that the auctorial model constructed in this study fits the characteristics of the Korean argumentative writing text.(Dalian University of Foreign Languages/China Research Center for Northeast Asian Languages·Dalian University of Foreign Languages)
Battle on Edge - Comparison of Convolutional Neural Networks Inference Speed on Two Various Hardware Platforms

Several reasons influenced the tendency to move the first level of machine learning data processing to the edge of the information system. Edge-generated data is typically processed by so-called edge devices with low processing power and low power consumption. In addition to well-known SoC (System on Chip) manufacturers that are usually used as an edge device, some manufacturers in this market base their processor design on open source. This paper compares two different SoC, one based on the ARM (Advanced RISC Machines) architecture and the other on the open-source RISC-V (Reduced Instruction Set Computer) architecture. The specificity of the analysed SoC based on the RISC-V architecture is an additional processor for speed up calculations common in neural networks. Since the architectures differ, we compare two SoC of similar price. The comparison’s focus is an analysis of the inference performance with the different number of filters in the first layer of the convolutional neural network used to detect handwritten digits. The process of convolutional neural network’s training occurs in the cloud and uses a well-known database of handwritten digits – MNIST (Modified National Institute of Standards and Technology). In the SoC based on the RISC-V architecture, a reduced dependence of the inference speed on the number of filters at the first level of the convolutional neural network was observed.
Frequent Subgraph Mining by Giraph Distributed System

To overcome the challenges for managing the rapid growth of social graphs, massive Distributed Graph Mining Systems are developed, such as Pregel, GiraphHama, GraphLab, PowerLab, etc. The common approach to all systems is to divide the entire Graph Dataset into smaller divisions and use it as “think like a vertex”, the programing model is to hold up a continual graph calculation. In this paper, we use the Optimized Frequent Subgraph Mining algorithm in the Giraph framework model and make a comparative study with existing different Distributed Systems. To enhance the flexibility and performance of the novel method, we carry out different optimization techniques associating it with updating different run time limits. We also investigate how the performance could be improved by Giraph Distribution System, which plays a vital role in social graphs such as LinkedIn, Twitter, Facebook, etc. The graph input, output, cluster set up and hardware configuration play vital roles in optimizing the performance of our proposed algorithm.
Toward Generic Abstractions for Data of Any Model

Digital data sharing leads to unprecedented opportunities to develop data-driven systems for supporting economic activities, the social and political life, and science. Many open-access datasets are RDF graphs, but others are CSV files, Neo4J property graphs, JSON or XML documents, etc. Potential users need to understand a dataset in order to decide if it is useful for their goal. While some datasets come with a schema and/or documentation, this is not always the case. Data summarization or schema inference tools have been proposed, specializing in XML, or JSON, or the RDF data models. In this work, we present a dataset abstraction approach, which () applies on relational, CSV, XML, JSON, RDF or Property Graph data; () computes an abstraction meant for humans (as opposed to a schema meant for a parser); () integrates Information Extraction data profiling, to also classify dataset content among a set of categories of interest to the user. Our abstractions are conceptually close to an Entity-Relationship diagram, if one allows nested and possibly heterogeneous structure within entities.