Reviewerly/exHarmony huggingface dataset:
Stochastic representation decision theory: How probabilities and values are entangled dual characteristics in cognitive processes

Humans are notoriously bad at understanding probabilities, exhibiting a host of biases and distortions that are context dependent. This has serious consequences on how we assess risks and make decisions. Several theories have been developed to replace the normative rational expectation theory at the foundation of economics. These approaches essentially assume that (subjective) probabilities weight multiplicatively the utilities of the alternatives offered to the decision maker, although evidence suggest that probability weights and utilities are often not separable in the mind of the decision maker. In this context, we introduce a simple and efficient framework on how to describe the inherently probabilistic human decision-making process, based on a representation of the deliberation activity leading to a choice through stochastic processes, the simplest of which is a random walk. Our model leads naturally to the hypothesis that probabilities and utilities are entangled dual characteristics of the real human decision making process. It derives two previously postulated features of prospect theory (Kahneman and Tversky, 1979): the inverse S-shaped subjective probability as a function of the objective probability and risk-seeking behaviour in the loss domain. It also predicts observed violations of stochastic dominance (Birnbaum and Navarrete, 1998) while it does not when the dominance is “evident”. Our theory, which offers many more predictions for future tests, has strong implications for psychology, economics and artificial intelligence.
Digital Communication Media: Resources and Distribution Channels on Forest and Land Fires Information in Riau Province of Indonesia During Covid-19 Pandemic Period

The control towards Indonesian forest and land fires in 2020 is deemed more complicated due to the Covid-19 outbreak Its rapid dissemination encourages the central and regional government officials to focus the Covid-19 risk reduction through some policies, such as health protocol, social activity and large-scale meeting restrictions as well as independent regional quarantine, causing the controlling process towards the fires to be implemented by collaborating a direct method in disaster-vulnerable areas through digital communication media The research is purposed to recognize the use of communication technology in controlling Indonesian forest and land fires during the Covid-19 pandemic period, comprising of qualitative research using data collection through interviews, observation, literature study, and online data searching It results the significance of digital communication media for information resources as well as the channel of the fires' information dissemination for internal task force and externals used by The Riau Province's Task Force of Forest and Land Fire Control The digital media are based on android system and website, such as Sipongi application, Info BMKG, LAPAN-Fire Hotspot, and Lancang Kuning Dashboard, while the others used for disseminating the information are Zoom meeting, Whatsapp, Instagram, Twitter, Facebook, and Youtube
Using YouTube Channel “BBC Learning English” to Improve Students’ Pronunciation (An Experimental Research at the Seventh Grade of SMPN 21 Kota Serang).

Tuti Tazkiyah. 152301803. 2019. Using YouTube Channel “BBC Learning English” to Improve Students’ Pronunciation (An Experimental Research at the Seventh Grade of SMPN 21 Kota Serang). The research investigates the use of YouTube channel BBC Learning English in students’ pronouncing the English consonants /ʃ/, /ʒ/, /tʃ/, /dʒ/, /θ/, and /ð/ at the seventh grade of SMPN 21 Kota Serang. The aims of this research were to know the students’ ability in pronouncing the English consonants /ʃ/, /ʒ/, /tʃ/, /dʒ/, /θ/, and /ð/ before and after using YouTube channel BBC Learning English at the seventh grade of SMPN 21 Kota Serang and to identify the effectiveness of using YouTube channel BBC Learning English in teaching pronunciation of consonants /ʃ/, /ʒ/, /tʃ/, /dʒ/, /θ/, and /ð/ at the seventh grade of SMPN 21 Kota Serang. The method used in this research was quasi experiment with the samples are 70 students. They are divided into two class, 36 students in experimental class and 34 students in control class. Furthermore, the writer used pre-test and post-test to collect the data. The result showed that the students’ pronouncing the English consonants /ʃ/, /ʒ/, /tʃ/, /dʒ/, /θ/, and /ð/ which using YouTube channel BBC Learning English had better performance than those who had not used YouTube channel BBC Learning English. After calculated the data using t-test, the value of tobservation was 5,36 with degree of freedom 68 in the degree significance 5% = 1,66 and in degree significance 1% = 2,38. It was found that to > tt = 5,36 > 1,66 in degree significance 5% and to > tt = 5,36 > 2,38 in degree significance 1%. It means was a significant effect of using YouTube channel BBC Learning English to improve students’ pronouncing the English consonants /ʃ/, /ʒ/, /tʃ/, /dʒ/, /θ/, and /ð/. Keywords: Pronunciation, YouTube, and YouTube channel “BBC Learning English”
Digitalization, the pandemics, information market and libraries – current realities and forecasts

In response to the advance of information and communication technologies and knowledge society, the whole new information environment and new technological life paradigm have been shaping. The vectors of digitalization are defined; the balance of printed and digital resources before and after the COVID-19 pandemic is reported. The information market processes under the pandemic are analyzed. Significant rise of online services with digital resources under the pandemic circumstances is indicated. The corresponding initiatives by major international professional organizations are discussed. The conclusion is made on the trends characterizing the current state and the immediate prospects for the information market.
Bias-Awareness for Zero-Shot Learning the Seen and Unseen.

Generalized zero-shot learning recognizes inputs from both seen and unseen classes. Yet, existing methods tend to be biased towards the classes seen during training. In this paper, we strive to mitigate this bias. We propose a bias-aware learner to map inputs to a semantic embedding space for generalized zero-shot learning. During training, the model learns to regress to real-valued class prototypes in the embedding space with temperature scaling, while a margin-based bidirectional entropy term regularizes seen and unseen probabilities. Relying on a real-valued semantic embedding space provides a versatile approach, as the model can operate on different types of semantic information for both seen and unseen classes. Experiments are carried out on four benchmarks for generalized zero-shot learning and demonstrate the benefits of the proposed bias-aware classifier, both as a stand-alone method or in combination with generated features.
Realization of the principle of methodological complementarity in the context of foreign language teachers' professional development

The analysis of functions of the principle of methodological complementarity in the frame of foreign language teachers' professional development was carried out in the article. The authors disclose terminological characteristics of the principle of complementarity and the principle of methodological complementarity, reveal the connections of the principle of methodological complementarity with methodological training and professional development of foreign language teachers. Further on, the variants of the principle of methodological complementarity realization in the frame of foreign language teachers' professional development, appropriate within several innovative organizational forms, are shown.
Library Sociology: Student course under new circumstances

The need for including the course of «Library sociology» into the student training program «Library and Information Activities», is substantiated. The experience of teaching Library Sociology at Moscow State Institute of Culture is discussed.
THE COMPARISON BETWEEN THE USE OF TOTAL PHYSICAL RESPONSE AND COMMUNITY LANGUAGE LEARNING TOWARD STUDENTS' VOCABULARY MASTERY AT SMPN 2 PATTALLASSANG

The objective of the research is to looked the influence of students’ vocabulary using two methods, namely Total Physical Response and Community Language Learning. It includes the different significant on students’ vocabulary mastery learnt by using Total Physical Response method comparing to the students’ vocabulary mastery learnt by using Community Language Learning. This research was conducted at SMPN 2 Pattallassang academic year 2019-2020 as the subject of research. The subject of this research was consisted of 31 students of second grade. The research was using an experimental method in the quantitative form by collecting data from test. The data collected in this research were analyzed by using t-test. According to the result of statistical calculation, the obtained value of t value (t-observation) is 2,16 and the value of t table (t table) from the df (30 ) on degree of significance of 5% is 2.04. Comparing the with each values of the degree of significance, the researcher was found 2.16 > 2.04 . It’s concluded that t value was higher than t table . Based on the result, hypothesis is accepted, it means that there is significance different of using Total Physical Response method and Community Language Learning method in teaching vocabulary. Based on the finding of this study it can be concluded that using Total Physical Response method is more effective than Community Language Learning method  in teaching vocabulary.
Ballistic Coefficient Estimation for Classification of Projectiles

The ballistic coefficient is a measure of a projectile’s ability to overcome air resistance. Because the motion equations of the projectile are functions of the ballistic coefficient, several studies estimating the projectile’s ballistic coefficient have been conducted to track its trajectory. In this paper, two methods are proposed to estimate the ballistic coefficient for classification of the projectile. The primary techniques used are the extended Kalman filter and ordinary differential equation parameter estimation. The true projectile trajectory is created via simulation, and the proposed method is used to estimate the ballistic coefficient. Furthermore their performances are compared and analyzed.
Parametric Density Estimation with Uncertainty using Deep Ensembles

In parametric density estimation, the parameters of a known probability density are typically recovered from measurements by maximizing the log-likelihood. Prior knowledge of measurement uncertainties is not included in this method -- potentially producing degraded or even biased parameter estimates. We propose an efficient two-step, general-purpose approach for parametric density estimation using deep ensembles. Feature predictions and their uncertainties are returned by a deep ensemble and then combined in an importance weighted maximum likelihood estimation to recover parameters representing a known density along with their respective errors. To compare the bias-variance tradeoff of different approaches, we define an appropriate figure of merit. We illustrate a number of use cases for our method in the physical sciences and demonstrate state-of-the-art results for X-ray polarimetry that outperform current classical and deep learning methods.
The Utilization of Big Data Technology in Handling Fake News (Hoax) Content

Big Data technology is a phenomenon and many organizations try to integrate it into their business processes to get added value and also support business processes.Big Data can be utilized in various ways, one of which is the handling of negatively charged Internet content.Along with the increasing dissemination of negatively charged internet content, especially hoaxes, the Ministry of Communication and Information facilitated various kinds of stakeholders in handling the hoax using the utilization of Big Data technology.Internet content monitoring carried out so far is through manual crawling, controlling negative content named Trust + based on Domain Name Server (DNS System) technology where the blocking mechanism can only be done by using a domain name and / or server name .By utilizing AIS machines that adopt Big Data technology, it can speed up the process of crawling negative content that has been done manually.The machine was not provided with a "killer weapon" site, social media accounts, news portals, and others.This study was conducted through a qualitative approach to describe the use of Big Data technology on AIS machines in monitoring negative internet content.The results of this study outline the use of Big Data technology in terms of handling negative internet content, especially dissemination of hoaxes.
Explore with Dynamic Map: Graph Structured Reinforcement Learning

In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation. Even so, learning and planning on such a map within a sparse environment remains a challenge. As a step towards this goal, we propose Graph Structured Reinforcement Learning (GSRL), which utilizes historical trajectories to slowly adjust exploration directions and learn related experiences while rapidly updating the value function estimation. GSRL constructs a dynamic graph on top of state transitions in the replay buffer based on historical trajectories, and develops an attention strategy on the map to select an appropriate goal direction, which decomposes the task of reaching a distant goal state into a sequence of easier tasks. We also leverage graph structure to sample related trajectories for efficient value learning. Results demonstrate that GSRL can outperform the state-of-the-art algorithms in terms of sample efficiency on benchmarks with sparse reward functions.
Adding Recurrence to Pretrained Transformers

Fine-tuning a pretrained transformer for a downstream task has become a standard method in NLP in the last few years. While the results from these models are impressive, applying them can be extremely computationally expensive, as is pretraining new models with the latest architectures. We present a novel method for applying pretrained transformer language models which lowers their memory requirement both at training and inference time. An additional benefit is that our method removes the fixed context size constraint that most transformer models have, allowing for more flexible use. When applied to the GPT-2 language model, we find that our method attains better perplexity than an unmodified GPT-2 model on the PG-19 and WikiText-103 corpora, for a given amount of computation or memory.
The Use of Liveworksheets.com in Designing English Learning Activity During Covid-19 Pandemic

The outbreak of the covid-19 virus at the beginning of 2020 has affected the world universally, including Indonesia. One of the aspects that have been affected the most is the education field. Claimed as the preventive action to slow down the covid-19 virus transmission in school, over 60 million students in Indonesia are temporarily forced by the new regulation to study at home. Thus, online learning is emerging as the only way for everyone in the education field to cope with this crisis. However, as a country that normally used a traditional class in the learning process, Indonesia has been struggling to adjust its education system with this situation. For that reason, this library research aims to explore the use of a web-based tool named liveworksheets.com in designing interactive English learning activity during the covid-19 pandemic. This research includes explanations of the advantages, disadvantages as well as solutions. It also offers examples of English learning activity to support the conclusion that liveworksheets.com can be used in online learning for enhancing the students’ skills in speaking, listening, and reading.
Indonesian Named Entity Recognition on An Employment Complaint Letter Using Machine Learning

This study determines the feature as a criterion for predicting the Named Entity(NE) in the labor complaint document. Seven elements are resulting from the feature extraction process for the NE classification process. This feature will be divided into three test cases in the classification. The classification process uses a variety of machine learning algorithms to determine which algorithm produces the best performance. The results show that Gradient Boosted Trees delivers the best performance based on accuracy.
Sound, Motion, and the Brain: exploring how sounds affect the creation of movements within an improvised performance

Exploration of the coupling of perception-action in the context of understanding creativity influenced by sounds in movement improvisation. Focusing on how recorded sounds (contemporary music and environmental sounds) affect the performer in the creation and execution of movements, and how this informs the creative process of performance.
A Blockchain-Based Reconstruction Framework for UAV Network

With the popularity of unmanned aerial vehicles (UAVs) and the development of the network technology, the UAV network has become a new research hotspot. However, due to various factors such as terrain, transmission distance, and hacker attacks, UAV network needs to reconstruct the network architecture and restore data according to the actual situation. The blockchain technology can be applied to the UAV network for its high reliability and distributed characteristics. Therefore, this paper proposes a blockchain-based adaptive reconstruction technology framework for UAV networks. Also, to solve possible data damage or missing problems in UAV networks, a data recovery and update approach as well as the corresponding algorithms are given. Furthermore, this paper uses the blockchain to record the behavior of the UAV nodes in the UAV network, and proposes an algorithm to check whether the UAV is malicious or malfunctioning. So that the ground control station (GCS) can reconstruct the UAV network communication link.
Privacy in CBDC technology

Privacy is a key aspect of a potential central bank digital currency system. We outline different technical choices to enact various privacy models while complying with the appropriate regulations. We develop a framework to evaluate privacy models and list key risks and trade-offs in privacy design.
Augmented Reality Contact Lenses: An Investigation of Technology Acceptance Drivers from a Potential User Perspective

Background: Augmented Reality (AR) could be the next revolutionary technology after the personal computer, the internet and the mobile era. While AR Smart Glasses are already on the market, AR Cont ...
MİZAH İÇERİKLİ INSTAGRAM POSTLARININ BİR REMİKS PRATİĞİ OLARAK TANIMLANMASI VE ANALİZİ

This paper investigates remix practice which is a popular method for creating content on the digital ecosystem. Within the scope of the paper, the remix has been analyzed in three dimensions: 1. as a way of building new meanings out of existing sources 2. as a way of generating user content 3. as for being in endless mobility. The history and applications of remix practices particularly the humoristic instances have been put under investigation. Throughout a month, all posts from three Instagram accounts have been collected afterwards the remix applications in these posts were identified. These findings were classified under three subtitles: 1. the remix implemented by the admin of the Instagram account, 2. the remix implemented by the followers of the account, 3. the remix which develops due to the mobility of the posts between social media environments. Frequency analysis has been conducted on 504 Instagram posts. In conclusion, these posts have been identified as humoristic, mobile, interactive, and multi-dimensional remix content.
Research guides: LibrarySearch Tips: Find Specific Titles

A guide to the University of Toronto Libraries' LibrarySearch platform How to search by title
Conditioned thermal states as nonclassical gates in subcycle sampling of quantum fields

Time-domain quantum electrodynamics is coming of age with recent demonstrations of direct probing of femtosecond quantum fields. Here, we propose to harness nonclassical light from bright entangled sources to dramatically enhance field-resolved measurements.
PENERAPAN FUZZY C-MEANS PADA KLASTERISASI KARAKTERISTIK PENGUNJUNG WEBSITE PMB STT – PLN UNTUK MENINGKATKAN KEPADATAN KUNJUNGAN

Bringing high website visitors is a difficult challenge to face because of the many interesting website contents popping up. This problem is also faced by the STT-PLN New Student Admission (PMB) website. One of the things that need to be considered in increasing website visitors is by knowing the demographic data of visitor characteristics. Changes in behaviour are also caused by the attractiveness of quality content that can attract visitor interest and can improve SEO (Search Engine Optimization) to be able to gain visitor density (traffic) so that it is easily indexed by Google's search engine. The data used in this study are location data, web browser data, parent attraction data and teenage attraction data of visitors to the PMB STT-PLN website. The method that can be done is to cluster the demographic characteristics of visitors using Fuzzy C-Means and Root Mean Square Error to test the accuracy of the cluster. Optimal accuracy results obtained for each data are when the browser data is grouped into 4 clusters with an error value of 0.000275229, location data into 2 clusters with an error value of 0.003197591, teenage attractiveness data into 4 clusters with an error value of 0.000322929, and data the appeal of parents into 2 clusters with an error value of 0.084997533.
If VNP is hard, then so are equations for it.

Assuming that the Permanent polynomial requires algebraic circuits of exponential size, we show that the class VNP does not have efficiently computable equations. In other words, any nonzero polynomial that vanishes on the coefficient vectors of all polynomials in the class VNP requires algebraic circuits of super-polynomial size. In a recent work of Chatterjee and the authors (FOCS 2020), it was shown that the subclasses of VP and VNP consisting of polynomials with bounded integer coefficients do have equations with small algebraic circuits. Their work left open the possibility that these results could perhaps be extended to all of VP or VNP. The results in this paper show that assuming the hardness of Permanent, at least for VNP, allowing polynomials with large coefficients does indeed incur a significant blow up in the circuit complexity of equations.
The Effect Of The Quizlet Application On The VocabularyMastery Of Students In Class VII MTs Al-Washliyah Bah Gunung

This study aims to determine the effect of the quizlet application on thevocabulary mastery observed and researched at Mts Al-washliyah Bah Gunung.This research is conducted using an experimental research design. The populationin this study are students of class VII MTs. Alwashliyah Bah Gunung. The sampleof the research is class VII-1 (24 students) as the control class and VII-2 (24students) as the experimental class. In this study, researchers taughtexperimentally using the quizlet application and in the control class, researcherstaught using conventional methods. Researchers used a pre test, treatment, andpost test in this study. In the control class, the score between the pre test and posttest increased by 18.12. Meanwhile, in the experimental class the score increasedby 21.87. Researcher uses SPSS v.22 to calculate the data. In the post-test controlclass and experimental class, the researcher finds that the Sig. (2-tailed) on the TTest is 0.114. > 0.05, so there is no significant difference between the controlclass and the experimental class. H0 accepted and Ha rejected. It is concluded thatthere is no an effect of students' ability in mastering vocabulary by quizletapplication.