Reviewerly/exHarmony huggingface dataset:
A Generalized Quadratic Loss for SVM and Deep Neural Networks

We consider some supervised binary classification tasks and a regression task, whereas SVM and Deep Learning, at present, exhibit the best generalization performances. We extend the work [3] on a generalized quadratic loss for learning problems that examines pattern correlations in order to concentrate the learning problem into input space regions where patterns are more densely distributed. From a shallow methods point of view (e.g.: SVM), since the following mathematical derivation of problem (9) in [3] is incorrect, we restart from problem (8) in [3] and we try to solve it with one procedure that iterates over the dual variables until the primal and dual objective functions converge. In addition we propose another algorithm that tries to solve the classification problem directly from the primal problem formulation. We make also use of Multiple Kernel Learning to improve generalization performances. Moreover, we introduce for the first time a custom loss that takes in consideration pattern correlation for a shallow and a Deep Learning task. We propose some pattern selection criteria and the results on 4 UCI data-sets for the SVM method. We also report the results on a larger binary classification data-set based on Twitter, again drawn from UCI, combined with shallow Learning Neural Networks, with and without the generalized quadratic loss. At last, we test our loss with a Deep Neural Network within a larger regression task taken from UCI. We compare the results of our optimizers with the well known solver $$\text {SVM}^{\text {light}}$$ and with Keras Multi-Layers Neural Networks with standard losses and with a parameterized generalized quadratic loss, and we obtain comparable results (Code is available at: https://osf.io/fbzsc/wiki/home/ ).
Cognitive Emotion Interaction Model of Robot Based on Game Theory

To solve the problems of the existing in the process of human-computer interaction system, such as lack of emotion and low participation, a cognitive emotion interaction model based on game theory in PAD emotion space is proposed. Firstly, the interactive input emotion of participant is evaluated and some influence factors such as friendship and resonance are extracted to analyze the current human-computer interaction relationship. Secondly, modeling the emotional generation process of participants and robots by simulating the psychological game process in interpersonal communication, and the optimal emotional strategy of the robot is obtained by using the sub-game perfection equilibrium of the embedded game. Finally, the emotional state transition probability of the robot is updated according the optimal emotional strategy. The spatial coordinates of the six basic emotional states are used as labels to obtain the PAD spatial coordinate of the robot emotional state after emotional stimulate, The results of experiment show that compared with the others emotional interaction model, the proposed model can reduce the dependence of robots on external emotional stimuli and effective guide participants to participate in human-computer interaction, which provides some ideas for the emotion cognition model of robot in human-computer interaction.
Employee Assessment Decision Support System Using the MOORA Method at the National University: Employee Assessment Decision Support System Using the MOORA Method at the National University

The quality of human resources is one of the supporting factors to increase the productivity of employee assessment of an agency. So from that high competent human resources can support the existence of employee appraisals, it will be known the achievements achieved by each employee. Decision Support System Application (SPK) was made to assist study programs in employee appraisal. One method of decision support systems is Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) which uses the calculation of criteria weights and the Analytical Hierarchy Process (AHP) method to assess employees. Currently, the National University is still using the manual method in the process of employee performance appraisal by calculating the score of each criterion using Microsoft Excel. The manual assessment process is very likely to occur errors in calculating each criterion and takes a long time in the calculation process. At present, the National University does not yet have a specific application used for employee performance appraisal. Therefore, a decision support system is built using a web-based Multi-Objective Optimization (MOORA) method in order to facilitate the process of evaluating objective employee performance based on weights and assessment criteria that have been determined.
Inflight Tracking Method with Beacon System and Scouting Drone

In this study, we designed an inflight tracking method that can track the current position and the flight route of the target drone through the beacon system and GPS from the scout drone in the beacon range of the target drone. The scouting drone can fly and approach to the inflight target drone to receive the beacon signal in range. If the scout drone receives the beacon signal from a target drone, it checks the beacon information and transmits it to the control center with its GPS data to identify and track the target drone. The inflight tracking method can calculate the current position and flight route of the target drone through the two or more GPS data and flight data at the check positions from the scout drone. The precision of the inflight tracking method can be affected by the critical distance and the number of check positions. To enhance the tracking precision, the scout drone checks two or three different check positions within the critical distance from the inflight target drone. The experimental results showed the proposed inflight tracking method has at least over 92% in the worst case of the experimental environment.
Per non morire di inerzia: piste di riflessione per una legge italiana su alcuni aspetti del fine vita

Taking as a starting point the current state of the Italian legislation and the latest capital judgement of the Constitutional Court in the area of the end of life, the article explores ‒ from a de iure condendo perspective ‒ several aspects of the laws and the reality of the health system and of the political culture of Belgium, a country that has highly developed care, services and rights in this area. This is followed by some considerations aiming at identifying possible paths of intervention by the Italian legislator and subsequently possibly prevent a further substitution by the Judiciary.
한국 근현대 과학사 연구의 성과와 과제: ‘한국 과학’에서 ‘과학 한국’까지

This paper reviews the achievements of Korea’s modern and contemporary history of science research and presents some tasks to be solved. First, the English abstracts of 106 modern and contemporary papers published in The Korean Journal for the History of Science are categorized into five topics through text mining. It also examines the central themes of the literatures in five periods: the late 19th century, the Japanese colonial era, liberation to the 1950s, the 1960s to the 1970s, after the 1980s, and North Korea’s science and technology. In particular, it introduces recently published articles and books. We argue that an understanding of the times should be fully reflected upon, and that comparative studies are necessary to overcome a mononational perspective. Furthermore, we propose expanding the scope of research by increasing contacts with adjacent areas and introducing quantitative methodologies that can handle vast amounts of data. In addition, while studies so far have focused on the formation and identity of ‘Korean science’, future research should clarify the ‘scientific Korea’ created by such science.
Educación secundaria en tiempos de pandemia: La voz de los estudiantes como recurso educativo

This article presents an educational experience that was carried out in the framework of the pandemic in a secondary school in the city of Trelew, province of Chubut. It presents one of the work proposals that was approached in the framework of the virtual classes in a school with an Oriented Cycle in Communication from the curricular spaces Languages II: Radio and Alternative Communication Practices and that was developed by 5th and 6th year students respectively. It also tells how, in addition to achieving several of the objectives defined from the curricular spaces, the proposed activity was a means to bring students, teachers and families together; a space where the voice of the students became an educational resource and where the radio was a bridge for these achievements. It is important to highlight that this activity allowed students to speak, listen to themselves and be heard by others. Also, this article presents fragments of the voices of the protagonists, the students themselves. Finally, the students' returns on all the work they did to solve the work proposal are presented and finally several reflections left by the experience of educating in pandemics are presented.
A SURVEY ON FEATURE SELECTION TO PERFORM CLASSIFICATION USING META HEURISTIC ALGORITHMS IN DATA MINING DOMAIN

Feature selection becomes the focus of much research in many areas of applications for which datasets with large number of features are available. Feature Selection Methods in Data mining and Data analysis problems aim at selecting a subset of the features that describe the data in order to get a compact and exact representation of the available information hidden in the data. The selected subset of data is the most useful and contains all the useful information with all the irrelevant data is deleted. The dimensionality of the feature space when being high affects the classification accuracies and the computational complexity due to redundant, irrelevant and noisy features present in the dataset. The role of feature selection is in the context of deep learning and how feature selection is related to feature engineering. Relevant feature identification has become an essential task to apply data mining algorithms effectively in real-world scenarios. Therefore, many feature selection methods have been proposed to obtain the relevant feature or feature subsets in the literature to achieve objectives of classification and clustering. A comprehensive overview, categorization, and comparison of existing feature selection methods are also done, and the guidelines are also provided to select a feature selection algorithm without knowing the information of each algorithm. Classification is a data mining (machine learning) technique used to predict group membership for data instances. Several major kinds of classification method include decision tree induction, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm and fuzzy logic techniques. The goal of this survey is to find the best feature selection method and classification technique to demonstrate the use of feature selection using classification in data mining. We conclude this work by identifying trends and challenges of feature selection research and development
LibGuides: McNair Scholars: Getting Started

A single access point for various OU Libraries' research guides and resources for McNair Scholars. Provides links to different getting started resources.
Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment

With the spread of smart devices, the use of big data, and the proliferation of the Internet of Things, virtualization technology for cloud servers have become important worldwide. Also, research has been conducted to efficiently manage the resources of hosts in VMs. Container-based virtualization has less performance degradation than VMs because there is no emulation for the operating system. Using the Docker API is slow to measure. In this paper, we implement the resource measurement module of Job nodes and design middleware that supports auto-scaling in auto-scaling module and Docker-based multi-host environment.
POSISI PEREMPUAN PADA TEKS WACANA SASTRA: PEMBACAAN ANALISIS KRITIS

This study aims to describe the position of women in the text of literary discourse. This research is a qualitative description. The data source in this study is the Ronggeng Dukuh Paruk novel by Ahmad Tohari and Tarian Bumi by Oka Rusmini. Data collection is done by observation, literature review, and recording techniques. The results showed that the choice of words used by male authors to describe the position of female actors in the Ronggeng Dukuh Paruk novel tended to marginalize women with bad (negative) portrayals and male actors were portrayed as ordinary. The event is fulfilled with the choice of refining words. While female authors in the Tarian Bumi novel provide identification to women with names that are inherent and described as they are and male actors are portrayed poorly. The event is described by the word dysphemism.
Mineral chemistry and geothermobarometry of the metapelites of Geysour metamorphic complex, east of Gonabad

Mineral chemistry and geothermobarometry of the metapelites of Geysour metamorphic complex, east of Gonabad
From Text-based to AR-based: an Investigation of Students' Motivation in Learning History Topics for Secondary School

History is a subject to which students may be resistant due to the nature of the content and the length of the texts involved. Studies had also shown that for this subject, students find it difficult to memorize facts and events. However, technologies may help to improve students' learning, if designed in a meaningful way. Augmented Reality (AR) has been widely used in many areas in enhancing students' learning and understanding. Among the advantages of AR is that it offers a fun way of learning and also triggers learners' motivation to learn abstract topics. This study aims to examine the challenges students face in learning history and their views on the use of AR-based learning materials in history. This single case study involved a group of form four students (16-year-old secondary school students) who scored less than 40 percent during their early year assessment. The data for this research was collected through one-to-one semi-structured interviews with all the respondents. The findings indicate factors the made learning History a challenge to the students. However, students showed positive feedbacks on the use of AR-based booklet. This paper further discusses the benefits of AR-based material to enhance students' engagement as well as the limitations of AR-based integration in teaching and learning. The findings of this study also highlight the importance of learning activities designed by educators to ensure that the planned activities have a positive impact on students' meaningful learning.n
Research on Adaptive Adjustment Algorithm of Isolated Word State Number Based on ANN-HMM Mixed Model

In order to overcome the contradiction between the recognition accuracy rate, training speed, and storage space caused by using the fixed state number as the mixed model state number, this paper conducts an experimental analysis on the traditional method of using the fixed state number as the model state number. A method for adaptively adjusting the number of model states is proposed above. Through theoretical analysis and simulation research, the method of selecting model state number is given, and a set of best state number suitable for the speech sample library used in this paper is determined. The simulation results show that the adaptive adjustment state number method proposed in this paper is in recognition accuracy and training speed. And the storage space is superior to its fixed state number method, which effectively improves the accuracy, stability and real-time performance of the speech recognition system.
Queen Bee Based Genetic Programming Method for a Hive Like Behavior

Designing the behavioral attributes of a robot is challenging, and the complexity of this task is even more increased in the case of swarm robotics. For effectively solving such problems special types of evolutionary algorithms can be used such as Genetic Programming and Queen Bee Based Genetic Programming method. The revolutionary idea behind these algorithms is that they use tree based representation for the individuals in a population, thus being able to solve structure optimization problems. The goal of this paper is to introduce the idea of the Queen Bee Based Genetic Programming method and compare its effectiveness with Genetic Programming through the evolution of a successful hive based behavioral program.
Transfer Learning for Chinese-Lao Neural Machine Translation with Linguistic Similarity

As a typical low-resource language pair, besides severely limited by the scale of parallel corpus, Chinese-Lao language pair also has considerable linguistic differences, resulting in poor performance of Chinese-Lao neural machine translation (NMT) task. However, compared with the Chinese-Lao language pair, there are considerable cross-lingual similarities between Thai-Lao languages. According to these features, we propose a novel NMT approach. We first train Chinese-Thai and Thai-Lao NMT models wherein Thai is treated as pivot language. Then the transfer learning strategy is used to extract the encoder and decoder respectively from the two trained models. Finally, the encoder and decoder are combined into a new model and then fine-tuned based on a small-scale Chinese-Lao parallel corpus. We argue that the pivot language Thai can deliver more information to Lao decoder via linguistic similarity and help improve the translation quality of the proposed transfer-based approach. Experimental results demonstrate that our approach achieves 9.12 BLEU on Chinese-Lao translation task using a small parallel corpus, compared to the 7.37 BLEU of state-of-the-art Transformer baseline system using back-translation.
에니어그램 성격유형에 기반 한 청소년 학습태도 척도

This study develops a youth learning attitude scale based on the Enneagram personality type for the purpose of identifying the characteristics of adolescents' own learning attitude according to their personality, providing a suitable learning environment for adolescents, and using it as data for learning guidance. Two research questions were set up and implemented. For the first research question, the item development of the youth learning attitude scale based on the Enneagram personality type was carried out through a total of the 3rd item selection process and the item verification process. The final 70 items were chosen through the selection process. For the second research question, three methods were performed to verify the learning attitude scale based on the Enneagram personality type. The first method was verified through the gender of adolescent males and females. The second method was compared with the Enneagram's three central energy thinking types, chest type, and belly type. The third method verified the retrospective method of college students. Cronbach's α coefficient was calculated to verify the reliability of the final scale. As a result, it was possible to verify and confirm the reliability and verification of 70 questions on the youth learning attitude scale based on the Enneagram personality type developed in this study.
Subjecthood and annotation: The cases of French and Wolof

This article considers the annotation of subjects in UD treebanks. The identification of the subjectposes a particular problem in Wolof, due to pronominal indices whose status as a pronoun or apronominal affix is uncertain. In the UD treebank available for Wolof (Dione, 2019), these have beenannotated depending on the construction either as true subjects, or as morphosyntactic features agreeingwith the verb. The study of this corpus of 40 000 words allows us to show that the problem is indeeddifficult to solve, especially since Wolof has a rich system of auxiliaries and several basic constructionswith different properties. Before addressing the case of Wolof, we will present the simpler, but partlycomparable, case of French, where subject clitics also tend to behave like affixes, and subjecthood canmove from the preverbal to the detached position. We will also make a several annotationrecommendations that would avoid overwriting information regarding subjecthood.
USING BUZZ GROUP TECHNIQUE IN TEACHING READING COMPREHENSION TO THE TENTH GRADE STUDENTS OF SMA MUHAMMADIYAH PAGARALAM

The main problem of this research “was there any significant difference in reading comprehension between students who were taught by using Buzz group technique and those who were not in the tenth graders of SMA Muhammadiyah Pagaralam?” The objective of this study was to find out whether or not there was any significant difference in reading comprehension between students who were taught by using Buzz group technique and those who were not in the tenth graders of SMA Muhammadiyah Pagaralam. The population of this research was all tenth grade students of SMA Muhammadiyah Pagaralam, consist of 259 students. Two classes were chosen as the sample and they divided into experimental class and control class that consist of 32 students each classes. The sample were taken by using purposive sampling. Quasi experimental method was used in this research. The result of pre-test and post-test in experimental class and control class were compared by using independent sample t-test, in term of reading aspects. The value of t -obtained was 4,628, at the significance value p &lt;0.05 in two tailed testing with df = 62, the critical value of the t- table was 1.999. Since the value of t -obtained was higher than the critical value of t -table, the null hypothesis (H 0 ) was rejected and the alternative hypothesis (H a ) was accepted. It could be concluded that there was significance different in the students’ reading comprehension in the experimental class at every aspect before and after treatment by using Buzz Group Technique.
Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty

Most annotation efforts assume that annotators will agree on labels, if the annotation categories are well-defined and documented in annotation guidelines. However, this is not always true. For instance, content-related questions such as ‘Is this sentence about topic X?’ are unlikely to elicit the same answer from all annotators. Additional specifications in the guidelines are helpful to some extent, but can soon get overspecified by rules that cannot be justified by a research question. In this study, we model the semantic category ‘illness’ and its use in a gradual way. For this purpose, we (i) ask many annotators (30 votes per item, 960 items) for their opinion in a crowdsourcing experiment, (ii) ask annotators to indicate their certainty with respect to their annotation, and (iii) compare this across two different text types. We show that results of multiple annotations and average annotator certainty correlate, but many ambiguities can only be captured if several people contribute. The annotated data allow us to filter for sentences with high or low agreement and analyze causes of disagreement, thus getting a better understanding of people’s perception of illness—as an example of a semantic category—as well as of the content of our annotated texts.