Reviewerly/exHarmony huggingface dataset:
글로벌 학습위기와 학습혁명 기회 (Leapfrog in Education)

Korean Abstract: 본고는 글로벌 학습위기를 가장 성공적으로 극복하는 학습혁명 선도국가를 한국 교육의 비전으로 제시한다. 먼저, 가장 중요한 방향으로 하이터치 하이테크 학습을 제시하고, 모두가 하이터치 하이테크 학습을 통하여 전인적이고 개별화된 평생학습을 할 수 있는 학생중심의 대량맞춤 학습체제를 실현하는 것을 학습혁명으로 정의하고 이미 미국을 중심으로 시작되고 있는 학습혁명의 사례들을 고찰한다. 이러한 논의를 바탕으로 본고에서는 한국이 글로벌 학습위기를 가장 선도적으로 극복하는 학습혁명 선도국가로 가기 위한 주요 방향과 전략을 제시한 후 최근 이슈가 되고 있는 학교의 자율성, 교육의 사회통합기능 강화, 대학의 파괴적 혁신 등의 관점에서 구체적 대안들을 논의한다. 마지막으로 최근 코로나19로 인한 위기를 오히려 학습혁명의 기회로 삼을 수 있는 대안들을 살펴보고 결론을 맺는다.English Abstract: This paper suggests that South Korea should lead global efforts of overcoming learning crisis and could become first-mover in leapfrog in education, which can be defined as processes of realizing personalized learning for all through High Touch High Tech education. After showing some notable demonstration cases of High Touch High Tech learning in US, discussions on how South Korea could nourish enabling ecosystem for High Touch High Tech education follows. This paper also discusses how High Touch High Tech education relates to autonomy of schools, disruptive innovation of university, and social integration through education. Lastly, we argue that South Korea could turn COVID-19 crisis into an opportunity for leapfrog in education by embracing cutting-edge EdTech and combine it with innovative pedagogy such as project-based learning.
Edge-cloud Collaborative Electric Power Inspection Image Target Detection Method

Electric power companies use UAVs, cameras, robots and other equipment for electric power transmission line inspections and image collection. Further, through artificial intelligence image detection technology, the images are processed to identify the defects or hidden dangers. Image detection model services are generally deployed on cloud platform, which is the cloud, or edge computing devices, which is the edge. On the one hand, if all images are processed at the edge nearby, the calculation will take a long time due to the limitation of edge computing capability. On the other hand, if all images are uploaded to the cloud for process, it will still take a long time when the bandwidth is limited. Transferring too much data will lead to inefficiency, and it is unrealistic to upload all data to the cloud. Edge-cloud collaborative electric power inspection image target detection method splits and deploys the image detection model services to the cloud and the edge. They are all involved in calculation, which reduces the amount of data transmitted and improves efficiency.
Building Machine Learning Application Using Oracle Analytics Cloud

Jain, Tarun Agarwal, Mahek Kumar, Ashish Verma, Vivek Kumar Yadav, AnjuThe main objective behind doing this work is that the future is autonomous and these systems improve analytics in cloud by providing “machine to human” environments. Oracle Analytics Cloud introduces the ability to “explain” an attribute in context of the other attributes and metrics in the dataset! Uncover what drives your results, be transparent with your findings, and analyze key segments of customer behavior. Hence with the growing amount of data in today’s world, an autonomous database would be perfect to handle it. The complete methodology adopted focuses on data preparation using the explain and other features of Oracle and then analyzing using different visuals after cleaning the data to get insights followed by creating different machine learning models and a small sample of natural language processing model. This entire paper focuses on bringing out the insights of sales that was further used to forecast the same for future, hence helping the company to strategize its sales all over. Through this paper, I have created several models which are further used on the autonomous database to answer the queries. Through this paper, I gained vast knowledge regarding various utilities of Oracle and how the technology is emerging which can help me in my future works. Oracle Analytics Cloud is seeking to go toe-to-toe with Microsoft (Power BI), Tableau and Qlik in particular. Why? All three vendors’ offerings are popular for BI and analytics. Moreover, they address many of the same areas as Oracle’s offering, including visual analysis and discovery, machine learning-driven data prep, and augmented analytics in the form of natural language queries. The tools used where Oracle Analytics Cloud, Oracle Analytics Desktop, Sql loader.
The Spammer Detection and Fake User Identification on Social Networks

Abstract: The Twitter has fleetly come an online source for acquiring real- time his/ her information about druggies. Twitter is an Online Social Network (OSN) where druggies can partake anything and everything, similar as news, opinions, and indeed their moods. Several arguments can be held over different motifs, similar as politics, Perticular affairs, and important events. When a stoner tweets commodity, it's incontinently conveyed to her followers, allowing them to unfold the entered information at a much broader position. With the elaboration of OSNs, the need to study and dissect druggies' actions in online social platforms has intensity Spammers can be linked grounded on (i) fake content, (ii) URL grounded spam discovery, (iii) spam in trending motifs, and (iv) fake stoner identification. And with the help of machine literacy algorithms we're going to identify the fake stoner and spammer in twitter. Keywords: Online Social Network, Spammers Identification, Fake User Identification.
Iets om over te praten. De problematiek van het voorzetselvoorwerp in het Nederlands en het Tsjechisch

For foreign students, the prepositional object is among the most problematic syntactic features of the Dutch language, as its form presents strong similarities with the adverbial clause, but it has an object function. In some cases it is very difficult to even decide whether the prepositional phrase has an adverbial function or is an object comparable with the direct object. In this paper the Dutch prepositional object is compared with similar constructions in the Czech language. Furthermore, the function of the preposition and its categorisation are briefly discussed. The paper ends with some criteria for Czech-speaking students to recognize a prepositional object as such.
Research and Course Guides: STCM 244: Research, Measurement, & Evaluation: Consumers

Library course guide for STCM 244: Research, Measurement, & Evaluation Demographic and consumer resources.
All-optical Scalable Spatial Coherent Ising Machine

We propose an all-optical spatial coherent Ising machine with a parametric cavity and spatial light modulator (SLM). We discuss how different SLM configurations realize different couplings, and then study the performance of the proposed machine.
Neural Translator Designed to Protect the Eastern Border of the European Union

This paper reports on a translation engine designed for the needs of the Polish State Border Guard. The engine is a component of the AI Searcher system, whose aim is to search for Internet texts, written in Polish, Russian, Ukrainian or Belarusian, which may lead to criminal acts at the eastern border of the European Union. The system is intended for Polish users, and the translation engine should serve to assist understanding of non-Polish documents. The engine was trained on general-domain texts. The adaptation for the criminal domain consisted in the appropriate translation of criminal terms and proper names, such as forenames, surnames and geographical objects. The translation process needs to take into account the rich inflection found in all of the languages of interest. To this end, a method based on constrained decoding that incorporates an inflected lexicon into a neural translation process was applied in the engine.
The Etheric Interpretation of Quantum Mechanics

In this paper, the physical explanation of all quantum phenomena such as wave function, wave function collapse, wave-particle duality, single-electron double-slit interference, uncertainty relation, tunneling effect and quantum entanglement is given by using the new etheric theory, and a new uncertainty relation which is consistent with the latest experimental results is given.
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators

We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy. We find that the performance of state-of-the-art offline and model-based RL methods degrade significantly given such limited data availability, even for commonly perceived solved benchmark settings such as MountainCar and CartPole. To address this challenge, we propose a model-based offline RL approach, called PerSim, where we first learn a personalized simulator for each agent by collectively using the historical trajectories across all agents prior to learning a policy. We do so by positing that the transition dynamics across agents can be represented as a latent function of latent factors associated with agents, states, and actions; subsequently, we theoretically establish that this function is well-approximated by a low-rank decomposition of separable agent, state, and action latent functions. This representation suggests a simple, regularized neural network architecture to effectively learn the transition dynamics per agent, even with scarce, offline data.We perform extensive experiments across several benchmark environments and RL methods. The consistent improvement of our approach, measured in terms of state dynamics prediction and eventual reward, confirms the efficacy of our framework in leveraging limited historical data to simultaneously learn personalized policies across agents.
Sequential Cross-Document Coreference Resolution

Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.
StoryDB: Broad Multi-language Narrative Dataset

This paper presents StoryDB - a broad multi-language dataset of narratives. StoryDB is a corpus of texts that includes stories in 42 different languages. Every language includes 500+ stories. Some of the languages include more than 20 000 stories. Every story is indexed across languages and labeled with tags such as a genre or a topic. The corpus shows rich topical and language variation and can serve as a resource for the study of the role of narrative in natural language processing across various languages including low resource ones. We also demonstrate how the dataset could be used to benchmark three modern multilanguage models, namely, mDistillBERT, mBERT, and XLM-RoBERTa.
Cardinal Metrics in Blockchain Functioning

Blockchain technology has been acquiring pace in deployments and implementations across globe vide association with large number of domains apart from widely known finance domain. These deployments are variegated in designs, have various architectures and possess functional differences. The commonality exists in deriving the benefits of blockchain technology through various technical variants of the widely known bitcoin blockchain architecture. Though still in evolving stage, the blockchain technology has been able to make an absolute mark in the industries, corporate and governance mechanisms to affirm that it’s part of a definite future. With devices estimate up to 50 billion in ecosystem of Internet-of-Things by 2025, the blockchain technology is soon going to be an integral part of future smart world. The deployment of any blockchain architecture might be able to accomplish the functional requirements as per design but the measurement of desired blockchain performance persists on a lot of parameters which need a balance and fine tuning established on purpose it has been designed for. In current times, transaction commit delays are being observed in bitcoin ecosystem. This paper identifies parameter effects on a bitcoin blockchain and measures the performance vide a bitcoin simulator effecting into tuning parameters like block size, blocks and number of nodes to analyze performance. The tuning effects into blockchain performance has been quantified, analyzed and discussed with focus on measuring and reducing the transaction propagation delays in a bitcoin environment. The paper concludes with heat map modeling plotted on Jupyter notebook with datasets derived.
THE USE OF TALKING CHIP TECHNIQUE IN IMPROVING EFL YOUNG LEARNERS’ SPEAKING SKILL AT SMP PLUS CAHAYA INSAN DEPOK JATIJAJAR

The objective of this action research study is to improve speaking ability of seventh grade of SMP Plus Cahaya Insan Depok, in academic year 2016/ 2017 through the implementation of talking chips Technique. This action was conducted in three cycles. Each cycle consisted of two meetings. The research steps are reconnaissance, planning, action, and reflection. The actions consisted of the implementation of talking chips technique, vocabulary practice, and pronunciation practice, the use of English Class during the teaching and learning process, and the use of media. The researcher worked collaboratively with the English teacher, the collaborator, and the students. The data were obtained from observations, tests, interviews, and discussions with the collaborator and the English teacher. The data were in the forms of vignettes, interview transcripts, students' scores, and photographs. To get the trustworthiness and the researcher used a triangulation technique. It was done by gathering data from a different point of view, including the students, the teachers, and the collaborators. The results of the research have shown that there is an improvement in the students' speaking ability through the implementation of the Talking Chips Technique. The students get more chances to speak and share their involvement during the teaching and learning process also improved. The students' mean scores showed improvements in all aspects of the speaking skill after the implementation of the actions. The aspects being assessed were pronunciation, fluency, grammar, vocabulary, and interaction. Keywords: Improving speaking ability, Talking Chips technique at SMP Plus Cahaya Insan Depok
Automatic Term Recognition Method for Military Domain

Abstract With the development of military intelligence, higher requirements are put forward for automatic term recognition in military field. In view of the characteristics of flexible and diverse naming of military requirement documents without annotated corpus, the method of this paper uses the existing military domain core database, and matches the data set and core database by Aho-Corasic algorithm and word segmentation technology, so that the terms to be recognized in the data set can be divided into three types. The possible rules of word formation of military terms are summarized and phrases that conform to the rules of word formation are found in the documents as the term candidate set. The core library and TF-IDF method are used to calculate the value of the candidate terms, and the candidate terms whose value is greater than the threshold are selected iteratively as the real terms. The experimental results show that the F1 value of this method reaches 0.719, which is better than the traditional C-value method. Therefore, the method proposed in this paper can achieve better automatic term recognition effect for military requirement documents without annotation.
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP.

Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks. However, the efficacy of meta-learning crucially depends on the distribution of tasks available for training, and this is often assumed to be known a priori or constructed from limited supervised datasets. In this work, we aim to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text, to enable large-scale meta-learning in NLP. We design multiple distributions of self-supervised tasks by considering important aspects of task diversity, difficulty, type, domain, and curriculum, and investigate how they affect meta-learning performance. Our analysis shows that all these factors meaningfully alter the task distribution, some inducing significant improvements in downstream few-shot accuracy of the meta-learned models. Empirically, results on 20 downstream tasks show significant improvements in few-shot learning – adding up to +4.2% absolute accuracy (on average) to the previous unsupervised meta-learning method, and perform comparably to supervised methods on the FewRel 2.0 benchmark.
PERCULARITIES OF ORGANIZATION OF LEARNING PROCESS IN THE CONTEXT OF STEAM EDUCATION TECHNOLOGY

In this research work about the problems of the learning process mathematics for students in the STEAM education is investigated. The STEAM technology of education allows creating in schoolchildren skills in conducting a laboratory experiment, understanding of fundamental scientific concepts and laws, ability to apply modern tools of activity, critical thinking and creativity.
Offensive language dataset of Croatian, English and Slovenian comments FRENK 1.1

The FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema in its details is described in https://arxiv.org/pdf/1906.02045.pdf. Usernames in the metadata are pseudo-anonymised and removed from the comments.The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.The difference to the first version of the dataset are the additions of 1. the annotation guidelines in English and 2. the link to the huggingface dataset.
Statistical relationships between the rule-making activity of state bodies in Russia

The article provides the analysis of the relationship between the rule-making activity of Russia’s state authorities: parliament, president, government and federal executive bodies. The analysis of Granger causality, carried out for statistical series of federal authorities rulemaking activity, indicates that the level of laws specification largely determines the level of by-laws specification (government decrees and orders of federal executive bodies), thereby exerting a significant impact on entire Russian regulatory framework volume. Econometric analysis based on a corpus of federal laws and aggregated corpus of regulations shows that the entire rule-making activity of the Russian parliament and federal authorities is explained by overlapping sets of factors. The findings indicate that to consolidate the effect of the “regulatory guillotine” it may be useful to develop mechanisms (procedural rules) that correct the balance between the pace and quality of developing legislative initiatives.
Nilai Moral Dalam Novel Gadis Kretek Karya Ratih Kumala (Tinjauan Sosiologi Sastra) Dan Relevansinya Dengan Bahan Ajar Sastra Di SMA

This study discusses (1) Describes the socio-historical background of Ratih Kumala (2) Describes the non-builder in the novel Kretek Gadis by Ratih Kumala (3) Describes the moral value of Ratih Kumala's Kretek (4) Describes the possible moral relevance of the Kretek Girls by Katih Ratih Kumala with literary teaching materials in high school Methods used This type of research uses a descriptive qualitative research design, the object of research is the moral value of the Kretek Girl novel. Data and Sources uses moral morals in the Kretek Girls novel and as primary data the Kretek Girls Novel by Ratih Kumala and secondary research that has been available and available indirectly containing books or scientific articles. The technique of collecting data in this study uses library, listening, and note taking techniques. Data validity or validity in this study uses triangulation techniques. Triangulation data is used to test the validity, reliability and data collection. The data analysis technique used in this study is the heuristic and hermeneutic semiotic model reader method. The results of this study are: (1) Ratih Kumala was born in Jakarta, June 4, 1980. Represented an Indonesian writer who studied at the Faculty of English Literature at Sebelas Maret University, Surakarta. (2) The structure in the novel that contains themes and facts of the story that is divided into plot, characterization, and setting. The theme in this novel is divinity. This novel has a forward plot. It is estimated that twenty-one figures recommended in the study ,. There are three settings discussed in this novel, they are time, place, and social culture. (3) Moral values in the Ratih Kumala Girls Kretek novel contain moral honesty, values that are approved, responsible, independent and critical (4) Relevant to learning in high school in KD 3.9 grade XII, learning the learning of three facts literature as language, psychology, and cultural background.
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks

Adversarial attacks based on randomized search schemes have obtained state-of-the-art results in black-box robustness evaluation recently. However, as we demonstrate in this work, their efficiency in different query budget regimes depends on manual design and heuristic tuning of the underlying proposal distributions. We study how this issue can be addressed by adapting the proposal distribution online based on the information obtained during the attack. We consider Square Attack, which is a state-of-the-art score-based black-box attack, and demonstrate how its performance can be improved by a learned controller that adjusts the parameters of the proposal distribution online during the attack. We train the controller using gradient-based end-to-end training on a CIFAR10 model with white box access. We demonstrate that plugging the learned controller into the attack consistently improves its black-box robustness estimate in different query regimes by up to 20% for a wide range of different models with black-box access. We further show that the learned adaptation principle transfers well to the other data distributions such as CIFAR100 or ImageNet and to the targeted attack setting.