Reviewerly/exHarmony huggingface dataset:
HUBUNGAN PENDIDIKAN DENGAN MASYARAKAT DALAM PERSPEKTIF SOSIOLOGI

Education and society have a very strong reciprocal relationship. This reciprocal relationship can be explained philosophically and sociologically by looking at each other's roles, and can also be explained by pedagogies. Community stratification has a role that influences the educational process, then education itself has a role and influence on the community stratification system. So basically, education and community stratification systems have an integral relationship with each other, the community is a unitary system that is interdependent and education-related is required to make continuous adjustments to the development of society. In accordance with the characteristics of the community, the education chosen by the community is education that can provide technological, functional, individual, informative and open capabilities.
Analysis of Achievement Standards of 2015 Revised Elementary Korean Curriculum based on Bloom’s Revised Taxonomy of Educational Objectives

본 연구는 현행 2015 개정 교육과정 <국어> 과목에서 초등학교 부분의 성취기준을 신 교육목표분류학을 토대로 분석해 봄으로써 국가 수준의 교육과정에서 제시하는 국어 교과의 교육목표가 얼마나 체계적으로 구성되었는지 분석하였다. 연구 결과는 다음과 같다. 첫째, 성취기준이 지식과 인지과정 차원의 여러 항목 중에서 ‘절차적 지식’과 ‘적용하다’에 편중되어 있어서, 너무 높은 수준으로 대강화되어 있었다. 둘째, 학생들의 인지 발달에 따른 학년별 위계도 고려되지 못한 것을 알 수 있었다. 이를 바탕으로 본 연구는 향후 새로운 교육과정 개정에 주는 시사점으로 다음과 같이 제안하였다. 첫째, 국어 교과의 하위 영역인 듣기·말하기, 읽기, 쓰기, 문법, 문학 각 영역별 특성을 고려하되, 신 교육목표분류학의 네 가지 지식 유형과 여섯 가지 인지과정 유형 항목을 고르게 반영하여 현재보다 더욱 구체적이며 체계적인 성취기준을 다양하게 구안하여 제시해야 한다. 둘째, 학년별 위계를 고려하여 1-2학년군에는 구체적이며 단순한 수준의 지식과 인지과정의 성취기준을 중심으로 구안하되, 학년이 올라갈수록 추상적이며 복잡한 수준의 지식과 인지과정의 성취기준이 많아지도록 제시해야 한다.This study analyzes the achievement standards of elementary school in the current 2015 revised curriculum <Korean>, based on Bloom s revised taxonomy, to find out how systematically the educational objectives of the Korean curriculum suggested in the national curriculum were structured. The results of the study are as follows. First, the achievement standards were concentrated on ‘procedural knowledge’ and ‘apply’ among the various items in the knowledge and cognitive process level, so they were reinforced to a high level. Second, it was found that the hierarchy of students according to their cognitive development years was not considered. Based on this, this study proposed the following as implications for future revision of new curriculum. First, while considering the characteristics of each area of listening, speaking, reading, writing, grammar, and literature, which are sub-area of the Korean subject, the four types of knowledge and six types of cognitive process of the new taxonomy are evenly reflected, and more than the present. Various concrete and systematic achievement standards must be devised and presented. Second, considering the hierarchies of each grade, the first and second grades should focus on the achievement standards of a specific and simple level of knowledge and cognitive processes, but as the grade goes up, the achievement standards of the abstract and complex level of knowledge and cognitive processes increase.
Safe Real-World Autonomy in Uncertain and Unstructured Environments

Author(s): Herbert, Sylvia Lee | Advisor(s): Tomlin, Claire J | Abstract: We are captivated by the promise of autonomous systems in our everyday life. However, ensuring that these systems act safely is an immense challenge: introducing complex systems into real-world uncertain environments while guaranteeing safety at all times is impossible in applications like self-driving vehicles, collaborative factory robots, and assistive robots. These systems will inevitably need to make real-time decisions with limited computational resources, and incomplete knowledge of the environment and other agents. This dissertation is an effort towards achieving trustworthy real-world autonomy by enabling autonomous systems to: (1) make theoretical safety guarantees efficiently based on known information, and(2) bridge the safety gap between this theory and the real world by reasoning about uncertainty in the environment and other agents.Towards this goal this dissertation covers various methods for scalable safety and real-time decision-making that draw from control theory, cognitive science, and learning, and are backed by both rigorous theory and physical testing on robotic platforms. We begin with an overview of reachability analysis and its applications for optimal control with safety guarantees. We then tackle the curse of dimensionality associated with reachability computation by decomposing systems or updating previous solutions in a warm-starting fashion. Next we explore planning in a simplified, low-dimensional space online with precomputed safety guarantees and tracking controls offline. This is extended to meta-planning, in which the online algorithm switches between faster/conservative modes and slower/accurate modes. Then we apply all of these tools towards navigating in uncertain environments among hard-to-predict agents such as human pedestrians. We use Bayesian machine learning methods to reason about human intention and navigate in a probabilistically safe manner. Finally, the dissertation ends with information about code bases and reachability tutorial examples.
Object registration using an RGB-D camera for complex product augmented assembly guidance

Augmented assembly guidance aims to help users complete assembly operations more efficiently and quickly through augmented reality technology, breaking the limitations of traditional assembly guidance technology which is single in content and boring in way. Object registration is one of the key technologies in augmented assembly guidance process, which can affect the location and direction of virtual assembly guidance information in real assembly environment. This paper presents an object registration method based on RGB-D camera, which combines Lucas-Kanade (LK) optical flow algorithm and Iterative Closet Point (ICP) algorithm. An augmented assembly guidance system for complex products through this method is built. Meanwhile, in order to compare the effectiveness of the proposed method, we also implemented object registration based on an open source augmented reality SDK Vuforia. An engine model and a complex weapon cabin equipment are taken as an case to verify this work. The result shows that the registration method proposed in this paper is more accurate and stable compared with that based on Vuforia and the augmented assembly guidance system through this method greatly improves the user's time compared with the traditional assembly. Therefore, we can conclude that the object registration method mentioned in this paper can be well applied in the augmented assembly guidance system, which can do enhance the efficiency of assembly considerably.
A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations

We present a large-scale corpus of e-mail conversations with domain-agnostic and two-level dialogue act (DA) annotations towards the goal of a better understanding of asynchronous conversations.We annotate over 6,000 messages and 35,000 sentences from more than 2,000 threads.For a domain-independent and application-independent DA annotations, we choose ISO standard 24617-2 as the annotation scheme.To assess the difficulty of DA recognition on our corpus, we evaluate several models, including a pre-trained contextual representation model, as our baselines.The experimental results show that BERT outperforms other neural network models, including previous state-of-the-art models, but falls short of a human performance.We also demonstrate that DA tags of two-level granularity enable a DA recognition model to learn efficiently by using multi-task learning.An evaluation of a model trained on our corpus against other domains of asynchronous conversation reveals the domain independence of our DA annotations.
Out-of-Task Training for Dialog State Tracking Models

Dialog state tracking (DST) suffers from severe data sparsity.While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications.In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers.
Identity Theft

Identity theft is a serious crime growing rapidly due to the ever-tighter integration of technology into people's lives. The psychological and financial loss to individual victims is devastating, and its costs to society at large staggering. In order to better understand the problem and to combat the crime more effectively, a comprehensive review of issues related to identity theft is performed in this paper. The human element of the crime is examined, along with the exploits used by perpetrators and countermeasures that have been developed. The findings highlight areas in need of continued research and guidelines that should benefit individuals and organizations in their pursuit of potential solutions.
Cyber Crimes against the State: A Study on Cyber Terrorism in India

The computer and network are utilized each and every aspect of humans wherein the internet gives equal opportunities for every aspect of human development. As the client of the internet becomes progressively differing and the scope of online communication extends, there is development in the digital violations for example break of online agreements, execution of online torts and violations, and so forth. The development of computer technology has many times proved that individuals and some groups in the country are using computer technology to threaten international governments and citizens of a country. This research paper clarifies the different types of cyber crime descriptively and particularly analyze cyber crimes against the state.
Prepping Your Data with Pandas

With the explosion of the Internet, social networks, mobile devices, and big data, the amount of data available is humongous. Managing and analyzing this data to derive meaningful inferences can drive decision making, improve productivity, and reduce costs. In the previous chapter, you learned about NumPy – the library that helps us work with arrays and perform computations, also serving as the backbone for the Pandas library that we discuss in this chapter. Pandas, the Python library for data wrangling, has the advantage of being a powerful tool with many capabilities to manipulate data.
Realization of a Tablet-Based Device for Body-Braille

The previous two-point Body-Braille (BB) device, which used two vibration motors, was not versatile and required a dedicated device. Currently, we study a one-point BB using a vibration speaker. However, only one Windows application is used as the vibration device. In this study, since many people have tablets, we propose to make those tablets into BB devices. The tablet also has a camera and an accelerometer, so we will be able to use them for past life support applications of BB. In this paper, we report that a tablet-based device was realized as a one-point BB device using a vibration speaker.
A Novel Color Multi-Secret Visual Cryptography Scheme

In this paper a color multi-secret visual cryptography scheme specifically for (3, 4, 5) access structure is proposed with random colors and XOR operation being leveraged to generate the sharing images. The recovery images with size invariant are obtained by the XOR operation of specific combination of shares. In order to achieve ideal perceptual quality, we present the optimization algorithm with which the visual quality of recovery images is improved significantly without sacrificing computation complexity. Experimental results demonstrate the effectiveness of the proposed scheme.
Development of a Unifying Theory for Data Mining Using Clustering Techniques

A performance evaluation of four different clustering techniques was carried out based on segmenting consumer by product type and by product usage in the research. Cobweb, DBSCAN, EM and k-means algorithms were evaluated based on the computational time, accuracy of the result produced and the purity of the result produced. The experiment was performed using WEKA as a data mining tool. The performance evaluation of the four techniques showed that K-means outperformed others in all considered evaluation measure while the EM technique was the second best in terms of accuracy and purity, outperforming the other two. DBSCAN technique was the 3rd best of the selected algorithms even as its computational time is shorter than that of EM while the fourth best performing calculation has been believed to be the Spider web calculation as respects to immaculateness, exactness and computational time.
Parallel Approaches of Utility Mining for Big Data

Utility Itemset Mining (UIM) is a fundamental technique to find out various itemsets with interestingness measures in addition to their quantity. It helps in finding valuable items that cannot be tracked with frequent itemset mining. There are many techniques to mine the itemsets based on their utilities, but the need of the hour is to mine them from larger datasets. This paper presents a brief overview of various approaches for utility mining, which mine using the parallel framework to enhance the pace of computation. The paper is concluded with a discussion on various challenges and openings in the field of parallel mining and provides away for further development of the prevailing methodologies of big data.
A FA-GWO-GRNN Method for Short-Term Photovoltaic Output Prediction

High-precision prediction of photovoltaic (PV) output is essential in PV system access to the power grid. To realize the security, stability and economic operation of power system, this paper proposes a hybrid factor analysis, gray wolf optimization, and generalized regression neural network (FA-GWO-GRNN) framework for short-term PV output forecast. In order to reduce the dimension of input features to PV output forecasting, the paper first develops a factor analysis (FA) to extract effective information from meteorological inputs. A generalized regression neural network (GRNN) algorithm is then employed to make the forecast, whose parameters are optimized by the gray wolf optimization (GWO) for its global searching capacity and fast convergence. The proposed GWO-GRNN framework effectively achieves high precision in short-term PV output forecasting, demonstrated in a case study on the measured power of a real world PV plant, which validated the accuracy and applicability of the proposed method in real-world scenarios.
Phase triangulation method for measuring 3D geometry of complex profile objects under dynamic noise

Abstract The paper proposes a method of phase triangulation for measuring the three-dimensional geometry of complex objects under conditions of dynamic noise. The method is based on statistical analysis of experimental data, adaptive filtering and use of a controlled source of structured exposure. Theoretical and experimental estimates of the error by the proposed method are presented in the paper. The method allows a reduction in the measurement error of the three-dimensional surface geometry of complex objects using phase triangulation methods.
Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer

Text style transfer aims to change an input sentence to an output sentence by changing its text style while preserving the content. Previous efforts on unsupervised text style transfer only use the surface features of words and sentences. As a result, the transferred sentences may either have inaccurate or missing information compared to the inputs. We address this issue by explicitly enriching the inputs via syntactic and semantic structures, from which richer features are then extracted to better capture the original information. Experiments on two text-style-transfer tasks show that our approach improves the content preservation of a strong unsupervised baseline model thereby demonstrating improved transfer performance.
The artistic worldview in the context of sociocultural realia

The article looks at specific features of the development and transformation of the artistic worldview in the context of changes taking place in art and sociocultural environment. The article focuses on the following issues: the interconnection between the core and periphery as the stable and variable spheres of art, the principles of relations between the subjective and objective, the general and individual, the rational and irrational. These contradictions are actively reflected in modern art and changing its expressive means. Therefore, in the general structure of the artistic worldview, the authors highlight the potential level (the flexible sphere of art) and conceptual level as the level where the integrity of the whole system is centered.
HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text

It is fairly common to use code-mixing on a social media platform to express opinions and emotions in multilingual societies.The purpose of this task is to detect the sentiment of codemixed social media text.Code-mixed text poses a great challenge for the traditional NLP system, which currently uses monolingual resources to deal with the problem of multilingual mixing.This task has been solved in the past using lexicon lookup in respective sentiment dictionaries and using a long short-term memory (LSTM) neural network for monolingual resources.In this paper, we (my codalab username is kongjun) present a system that uses a bilingual vector gating mechanism for bilingual resources to complete the task.The model consists of two main parts: the vector gating mechanism, which combines the character and word levels, and the attention mechanism, which extracts the important emotional parts of the text.The results show that the proposed system outperforms the baseline algorithm.We achieved fifth place in Spanglish and 19th place in Hinglish.The code of this paper is availabled at : https://github.com/JunKong5/Semveal2020-task9
Stillheten er lydsinnets himmelhvelving

This paper contemplates on music as a dialectic between moving energy and fixed structure, metaphorized by the image of streaming water that rushes against stones in the riverbed and, as a result, produces whirling shapes. As a pattern, this dialectic manifests itself in the mind of the creating artist as the process of condensing fleeting inspiration into fixed forms. Music is described as being a verb, not a noun, in the sense that it presupposes expressive energy; however, through the reification in the concept of a specific work it nevertheless obtains the status of a noun. The notion of energy as the foundation for all sound and sound production is conspicuously absent in current music theory. It is the mission of serious music to awaken the audience to existential and transcendental realization. Serious music is not a particular style or genre, but is universally characterized by depth. Depth can be understood in two ways: It is both structural, referring to the multidimensionality intrinsic to the music itself, and existential, referring to the awakening potential of the music.
Ontology Based System for Prediction of Diseases

&lt;p&gt;In today’s world of lifestyle, biomedical and healthcare act the main role through which disease in the patient can be identified. However, the current solution focuses on communities where the accurate prediction plays a major role to find out risk of the disease in the patient. The detection of disease is done by using prediction algorithm. Here, machine-learning algorithm is has been used to find the accuracy. The dataset has been is collected from certain hospitals and pre-processed where the missing values have been reconstructed before prediction process. Due to the huge amount of information in healthcare, the accurate result is the need for disease recognition and services. Generally raw data has bad quality because it does have exactness, completeness of records fields. Moreover, there would be different exhibits in different regions, the appearances of certain diseases, which may also weaken the prediction of the disease outbreak. Using the health record, our system received the rate of accuracy is 97%. In this proposed system, we provides prediction of various diseases that occurs through using machine learning that will be effective. In urban lifestyle, modern large cities have significant adverse effects on health, &amp; increasing risk of diseases.&lt;/p&gt;
PARTICIPATION REPORTS TOOL: OPPORTUNITIES AND PROSPECTS

This article examines a new tool recently proposed by the international Crossref database. Participation Reports aims to improve publicly available metadata of scientific publications as well as provide publishers with new operational tools to support organizational decisions. This new service will be useful primarily to organizations, which provide depositing services to publishers and are intermediaries between them and Crossref database. With the help of the Participation Reports service, this range of services can be significantly expanded because of availability of the information about correctness of the entered metadata as well as the number of links to downloaded material and the possibility of cross-references. The use of the service is illustrated by the example of general academic journals of the National Academy of Sciences of Ukraine.
Multiple Oracle consensus for weakly supervised defect detection in concrete structures using audio data

Inspection of critical social infrastructures, which are mainly made of concrete, is a pressing issue. Weakly supervised methods are interesting for such critical tasks because they allow a unique mix of human involvement and automation. However, humans can make mistakes, resulting in the system being misled. In the present paper is proposed a framework for weakly supervised defect detection in concrete structures involving a consensus between several humans providing weak supervision. This allows to compensate for the shortcomings of each individual human and, therefore, yield better performance along with robustness to erroneous weak supervision. Experiments conducted with concrete test blocks in laboratory conditions showed the effectiveness of our proposed method.
Computer Based Assessment: an Innovation to Language Assessment

With the integration of technology and education, classroom teaching has experienced a substantial change. In order to improve the efficiency of assessment, computer have been adapted as promising instruments in assessment area. This paper aims at evaluating the role that computer can play in language assessment.
A Novel End Effector of Environmental Exploring Robot with Fully Spherical Workspace

Yu, S.; Zhang, J., and Li, W., 2020. A novel end effector of environmental exploring robot with fully spherical workspace. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 159–164. Coconut Creek (Florida), ISSN 0749-0208.A large workspace is an important parameter to improve the robot's mobility and flexibility. Based on decoupled 2-degree of freedom spherical parallel mechanism, two novel end effectors of the environmental exploring robot with a fully spherical workspace was presented. Through workspace analysis, the conditions of the end effectors to achieve a fully spherical workspace were determined. Through kinematic analysis, the kinematic equations of the two end effectors were established. After size optimization and performance comparison, the input and output performance of the RR&P5R end effector is better. Then the statics analysis and finite element analysis of the RR&P5R end effector were carried out. The results show that the material selection and structural design are reasonable. This research provides a technical support for complex unknown environment exploration.