Reviewerly/exHarmony huggingface dataset:
Development of Augmented Reality for Biology E-Magazine

The purpose of this research is to design an electronic magazine design so that students are interested in participating in the learning process. Many students find it difficult to understand biology concepts because they cannot see it directly. We can use Augmented Reality technology in e-magazine design as one of these innovations. Therefore, this descriptive research was conducted to design an electronic magazine based on Augmented Reality. The development process was carried out in two stages, namely analysis and design. The analysis phase was carried out by observation using a questionnaire and the design phase was carried out by utilizing various computer software. The result of this research was an e-magazine design that is integrated with Augmented Reality, so that students can see abstract biological objects by 3D Augmented Reality directly with the technology. These results are expected to be developed and conduct further testing
Designer Modeling through Design Style Clustering.

We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create roguelike games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms. We further discuss how the results here can be used to create style-sensitive suggestions. Such suggestions would allow the system to be one step ahead of the designer, offering suggestions for the next cluster, assuming that the designer will follow one of the archetypical design traces.
Insights from Generative Modeling for Neural Video Compression

While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images. In a similar spirit, we view recently proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We present these codecs as instances of a generalized stochastic temporal autoregressive transform, and propose new avenues for further improvements inspired by normalizing flows and structured priors. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video and discuss their tradeoffs and ablations. In particular, we propose (i) improved temporal autoregressive transforms, (ii) improved entropy models with structured and temporal dependencies, and (iii) variable bitrate versions of our algorithms. Since our improvements are compatible with a large class of existing models, we provide further evidence that the generative modeling viewpoint can advance the neural video coding field.
Fuzzy Time Series Techniques for Forecasting

The domain of statistics and data sciences empowers us to design and develop applications that help us in the real world. From this perspective, fuzzy logic is a reward that can be used for a wide range of statistical applications related to control theory and artificial intelligence. More recently, fuzzy methods have been suggested as substitutes for conventional methods like linear regression, statistical quality control and time series forecasting. In spite of an emerging literature pertaining to the progress and application of fuzzy techniques in statistical analysis, there is a need for more organized insight into the possibilities of cross-fertilization between statistics and fuzzy logic. The main aim of this chapter is to provide an overview of the combination of statistical methods and fuzzy set theory. Traditional time series models like Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) are incapable of dealing with problems where historical data are available in the structure of linguistic constructs in spite of being in the form of numerical values. Various new methodologies have been proposed for dealing with these forecasting problems by means of fuzzy time series.
Tacoma Community College Library: ABE 69: HS 21+ Fitness (PE): Writing annotations

This guide provides links to the most useful TCC Library databases and resources for the PE credit module in the ABE 69 HS 21+ portfolio course. What is an annotation, why should I write one, what should be included and what is MLA?
EFFECTS OF VR CONTENTS ON PROBLEM-SOLVING IN PHYSICAL EDUCATION

1 Tokyo Gakugei University (JAPAN)2 Doctoral Course of Tokyo Gakugei University (JAPAN)3 Teikyo University (JAPAN)
Improvement of Student Attention Monitoring Supported by Precision Sensing in Learning Management Systems

A Learning Management Systems (LMS) can benefit from the inclusion Computer-Mediated-Communications (CMC) software for delivering materials. Incorporating CMC tools in virtual classrooms or implementing educational blogs, can be very effective in e-learning platforms. In such student-centered interaction scenarios, it is important to monitor and manage student attention in a precise way to enhance student performance. Sensing with precision through 6G/7G technology allows to include electronic and software devices to produce such monitoring. This chapter contextualizes and describes an abstraction application scenario of sensing and monitoring student attention with high precision in Learning Management System with new communication systems. In that context, technology (e.g. sensors), is used to perform automatic attention monitoring, helping to manage students in e-Learning. Additionally, the document presents a possible scenario which supports intelligent services to the monitoring of student attention during e-learning activities in the context of Smart HEI (Higher Education Institutes).
Politeness in Language of Riau Malay Students: A Linguistic Phenomenon

This study aims to gain an in-depth understanding of the phenomenon of politeness in the language of Riau Malay students. This study was qualitative with a phenomenological method. The data were obtained through recordings of student speech acts and semi-structured interviews with 48 students. Data analysis phenomenological data analysis was carried out in several stages, namely (a) Initial stage: fully describing the phenomena experienced by the research subject. All recorded speech act data and in-depth interviews with research subjects were transcribed into written language. (b) In this study, the reduction stage was carried out in two ways: eudetic reduction and phenomenological reduction. Eudetic reduction is made by reducing the recorded speech act data that has been transcribed from spoken language to written language. In eudetic reduction, values, ideologies, norms, or meanings will be found from the students' recorded speech acts. Meanwhile, the phenomenological reduction carried out on interviews with students related to politeness in speech acts. The results showed that the politeness phenomenon that occurs in student speech is motivated by several factors. First, different views from students about politeness are motivated by culture and the environment in which they live. Second, the habit of speaking in the way they speak. Third, their understanding of the narrative context.
INFOGRAPHIC UTILIZATION OF FLOWS TO IMPROVE UNDERSTANDING OF PASSPORT APPLICATIONS THROUGH SOCIAL MEDIA IN THE CLASS II IMMIGRATION OFFICE OF TPI CILACAP

&#x0D; &#x0D; Infographic comes from the English word Infographic which stands for information and graphic, which means a form of information visualization that delivers complex data to the reader so that it can be understood more easily and quickly. The purpose of this research is to find out how to use the Infographic at the Class II Immigration Office of TPI Cilacap and to find out how the public understands the passport application with the Infographic at the Class II Immigration Office of TPI Cilacap. This research provides information and understanding about passport application to the public through Infographics at the Class II Immigration Office of TPI Cilacap and provides input on consideration and evaluation in the utilization of inforafis at the Class II Immigration Office of TPI Cilcap. In this research using qualitative research method. The results of the study are known to the average resource person between 18 - 35 years old, this can be said as a young reader or digital natives and it is known that the Class II Immigration Office of TPI Cilacap innovates the form of content in its instragram account into Infographics. The Class II Immigration Office of TPI Cilacap uses the Infographic as a medium to convey various kinds of information about immigration through social media. In the case of making Infographics Of Class II Immigration Office TPI Cilacap needs to follow the development of existing trends.&#x0D; &#x0D; &#x0D;
Empirical Support for a Rate-Distortion Account of Pragmatic Reasoning

Author(s): Zhou, Irene; Hu, Jennifer; Levy, Roger; Zaslavsky, Noga | Abstract: Iterated models of pragmatic reasoning, such as the Rational Speech Act model (RSA; Frank a Goodman, 2012), aim to explain how meaning is understood in context. We propose an optimal experiment design approach for teasing apart such models, in which contexts are optimized for differentiating model predictions in reference games. We use this approach to compare RSA with RD-RSA (Zaslavsky et al., 2020), a recent variant of RSA grounded in Rate-Distortion theory. First, we show that our optimal experiment design approach finds cases in which the two models yield qualitatively different predictions, in contrast to previous experimental settings for which these models generate similar predictions. Next, we test the models on newly collected experimental data using our optimal design. Our results show that in this experimental setting RD-RSA robustly outperforms the standard RSA model. This finding supports the idea that Rate-Distortion theory may characterize human pragmatic reasoning.
Human Learning from Artificial Intelligence: Evidence from Human Go Players' Decisions after AlphaGo

Author(s): Shin, Minkyu; Kim, Jin; Kim, Minkyung | Abstract: Although Artificial Intelligence (AI) is expected to outperform humans in many domains of decision-making, the process by which AI arrives at its superior decisions is often hidden and too complex for humans to fully grasp. As a result, humans may find it difficult to learn from AI, and accordingly, our knowledge about whether and how humans learn from AI is also limited. In this paper, we aim to expand our understanding by examining human decision-making in the board game Go. Our analysis of 1.3 million move decisions made by professional Go players suggests that people learned to make decisions like AI after they observe reasoning processes of AI, rather than mere actions of AI. Follow-up analyses compared the decision quality of two groups of players: those who had access to AI programs and those who did not. In line with the initial results, decision quality significantly improved for the players with AI access after they gained access to reasoning processes of AI, but not for the players without AI access. Our results demonstrate that humans can learn from AI even in a complex domain where the computation process of AI is also complicated.
Common Origins of Social Interaction of Different Species: The Model of Coherent Intelligence Linking Physics to Social Sciences

Author(s): Danilov, Igor Val.; Mihailova, Sandra | Abstract: This paper studies social interaction in newborns and of various species, whose behavioral developments attributed to the circular sensory motor Stage 3 of behavior development. The article has associated 16 exciting facts of the social behavior in groups of different species, highlighting three questions: (1) how can newborns successfully classify social phenomena that are abstract or absent from their reality; (2) whether emotional contagion can appear through cues of body language that subjects cannot consciously perceive; (3) how do organisms distinguish identical stimuli by their importance (value) without perceptual driver stimuli. The analysis suggests that organisms can interact at initial stages of development by distinguishing cues of a similar modality by their significance (their value). This ability contributes to the assimilation of knowledge about the initial social phenomena. This paper proposes the model of Coherent intelligence, which is strictly based on experimental evidence in modern literature and laws of physics.
Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation

While self-training has advanced semi-supervised semantic segmentation, it severely suffers from the long-tailed class distribution on real-world semantic segmentation datasets that make the pseudo-labeled data bias toward majority classes. In this paper, we present a simple and yet effective Distribution Alignment and Random Sampling (DARS) method to produce unbiased pseudo labels that match the true class distribution estimated from the labeled data. Besides, we also contribute a progressive data augmentation and labeling strategy to facilitate model training with pseudo-labeled data. Experiments on both Cityscapes and PASCAL VOC 2012 datasets demonstrate the effectiveness of our approach. Albeit simple, our method performs favorably in comparison with state-of-the-art approaches. Code will be available at https://github.com/CVMI-Lab/DARS.
A-star path planning simulation for UAS Traffic Management (UTM) application

This paper presents a Robot Operating System and Gazebo application to calculate and simulate an optimal route for a drone in an urban environment by developing new ROS packages and executing them along with open-source tools. Firstly, the current regulations about UAS are presented to guide the building of the simulated environment, and multiple path planning algorithms are reviewed to guide the search method selection. After selecting the A-star algorithm, both the 2D and 3D versions of them were implemented in this paper, with both Manhattan and Euclidean distances heuristics. The performance of these algorithms was evaluated considering the distance to be covered by the drone and the execution time of the route planning method, aiming to support algorithm's choice based on the environment in which it will be applied. The algorithm execution time was 3.2 and 17.2 higher when using the Euclidean distance for the 2D and 3D A-star algorithm, respectively. Along with the performance analysis of the algorithm, this paper is also the first step for building a complete UAS Traffic Management (UTM) system simulation using ROS and Gazebo.
Long Short Term Memory on Bitcoin Price Forecasting

In modern times, many people rely on sophisticated technology to meet their needs. Already many technologies today can replace the role and function of society in the field of investment. There are many ways to fulfill the lives of these people, such as Bitcoin investment. Bitcoin is a digital asset that only exists in digital form by means of peer-to-peer work. To maximize profits, it is necessary to forecast Bitcoin prices when it will go up or down. This study tries to address the changes in Bitcoin prices whether to go up or down the next day with an artificial neural network model. The editor used in this study is the LSTM method. The data used is the Bitcoin blockchain data, namely time-series data in a one-day period from 1 January 2018 to 31 May 2019. Obtained forecasting results in June 2019 for Bitcoin to rise slowly and an accuracy value of 97.5% based on MAPE with the first day worth $8901.50.
Formation of foreign language writing skills based on formative assessment technology (B1 level)

The article discusses the features of the formation of students ‘ skills of writing a foreign language at the level of B1. Writing skills arose on the basis of the sound speech of the human race, as a mirror of the culture and traditions of each country, in the knowledge and accumulated experience of the individual. It plays an important role in teaching a foreign language This article differentiates the effectiveness of formative assessment technology as a way of mastering writing skills in a foreign language.Information was provided on the role of writing skills and their features, which are important when teaching a foreign language. In addition, the article shows the mechanism of improving teaching and learning – the im- portance of formative assessment in the educational process and the types of technologies of formative assessment, ways of its application. Shown the goals of the components of formative assessment used to measure progress, the level of learning by students in a daily lesson. When assessing students based on the technology of formative assessment, different forms of assessment are used: peer assessment, self-assessment, group assessment. The results of the study revealed the impact of the use of formative assessment on the improvement of writing skills in a foreign language and the effectiveness of various methods used in this context.Key words: language, writing skills, foreign language, formative assessment, technology, writing, speech, level, rule.
Cloud Technologies in Teaching Conference Interpreting

Global COVID-19 crisis requires a modern approach to teaching. One way to modernize the work of a teacher and to respond to new challenges is to use cloud services. This article deals using cloud services for teaching conference interpreting.Cloud technologies include various hardware, software, methodologies and tools like Internet services provided to the user for fulfiling certain tasks and projects. Cloud computing is a distributed data processing technology in which computer resources and capacities are provided to the user as an Internet service, i.e. the user is provided with a platform on the Internet that is physically located on a remote server.Using cloud technologies for teaching has a number of advantages. They include:1) accessibility from any device with Internet access.2) scalability of the system at the request of the user.3) flexibility of settings and services.4) integration of various services on one platform.5) security ( mandatory encryption and data backup algorithms).6) cost-effectivenessEffective training of conference interpreting skills implies combining a number of cloud services.First, it is the webinar platform. There is a number of cloud webinar platforms that provide a great number of options necessary for teaching conference interpreting. It is worth saying that most modern webinar platforms have the same number of options we need, for example, webinars, chat, video and audio calling, screen sharing. Nevertheless, the webinar platform cannot provide us with all the tools we need for teaching conference interpreting, that is why we need additional services.Second, the additional services we need for teaching, include: cloud data storage, online video hosting, online voice recorder.A typical conference interpreting lesson implies unseen translation, special exercises for improving the basic skills necessary for interpreting ( active listening, analysing the message and reproducing it in another language) and oral translation of audiovisual texts. Besides, cloud technology allows role-playing (one of the students gives a speech on the topic of the lesson while the rest of the group translate it either simultaneously or consecutively).All in all, cloud services allow us to face the global crisis and keep on teaching even so complex disciplines as conference interpreting, online.
Pragmatic factors can explain variation in interpretation preferences for quantifier-negation utterances: A computational approach

Author(s): Attali, Noa; Scontras, Gregory; Pearl, Lisa S | Abstract: Traditional investigations of quantifier-negation scope ambiguity (e.g., Everyone didn't go, meaning that no one went or not everyone went) have focused on universal quantifiers, and how ambiguity in interpretation preferences is due to the logical operators themselves and the syntactic relation between those operators. We investigate a broader range of quantifiers in combination with negation, observing differences in interpretation preferences both across quantifiers and also within the same quantifier (confirmed by corpus analysis). To explain this variation, we extend a computational cognitive model that incorporates pragmatic context-related factors, and which previously accounted for every-negation, to predict human interpretation preferences also for some and no. We evaluate the model's predictions against human judgments for quantifier-negation utterances, finding a strong qualitative and quantitative match when the listener has particular expectations about the world in which the utterance occurs. These results suggest that pragmatic factors can explain variation in interpretation preferences.
A Survey on Role-Oriented Network Embedding

Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance in various of graph mining tasks including link prediction and node clustering and classification. A wide variety of NE methods focus on the proximity of networks. They learn community-oriented embedding for each node, where the corresponding representations are similar if two nodes are closer to each other in the network. Meanwhile, there is another type of structural similarity, i.e., role-based similarity, which is usually complementary and completely different from the proximity. In order to preserve the role-based structural similarity, the problem of role-oriented NE is raised. However, compared to community-oriented NE problem, there are only a few role-oriented embedding approaches proposed recently. Although less explored, considering the importance of roles in analyzing networks and many applications that role-oriented NE can shed light on, it is necessary and timely to provide a comprehensive overview of existing role-oriented NE methods. In this review, we first clarify the differences between community-oriented and role-oriented network embedding. Afterwards, we propose a general framework for understanding role-oriented NE and a two-level categorization to better classify existing methods. Then, we select some representative methods according to the proposed categorization and briefly introduce them by discussing their motivation, development and differences. Moreover, we conduct comprehensive experiments to empirically evaluate these methods on a variety of role-related tasks including node classification and clustering (role discovery), top-k similarity search and visualization using some widely used synthetic and real-world datasets...