Reviewerly/exHarmony huggingface dataset:
On high school students' reading habits and teaching literature

The introductory part of the paper discusses the changed reading habits of children and young people, conditioned by various factors - the development of modern technologies, the use of the Internet, constant reading of various content from numerous screens, changed thought patterns, "accelerated" time, etc. Such a context leaves little room for reading books and other printed publications, especially the so-called fine literature (fiction). For this reason, our research focus is on the attitude of high school students towards books and reading within the framework of teaching literature, and beyond, in the sphere of private life. The aim is to examine their reading habits and attitudes about books and reading in order to - by comparing the obtained data with the results of the similar research - determine the nature of the changes that have occurred in the meantime, and that new knowledge can be used to create serious, fact-based, methodological strategies for improving the current analyzed situation. The central part of the paper offers the presentation of the research conducted on a sample of 1177 students from 13 high schools in the area of the cities of Niš and Novi Pazar, conducted in late 2018 and early 2019. An anonymous questionnaire was used to collect and then interpret the data on students' family and school habits related to books and reading, their attitude towards school compulsory reading lists, libraries, e-books and online literature, as well as creativity and literature studies.
Serendipitous Learning Fostered by Brain State Assessment and Collective Wisdom

Serendipitous discovery, invention or artistic creation are among the most exciting and utmost relevant phenomena strongly related to human learning. At the moment, there are very few measurable criteria helping to understand and foster serendipity. In other papers [, , ] we have presented, discussed and exemplified a new paradigm/model/method/system/environment – called ViewpointS – that represents our efforts to overcome many current existing limitations in generic Information Systems or search engines (e.g.: Google) as well as in other social media (e.g.: recommender systems) offering information retrieval solutions based on the proximity of available resources. We also have also exposed how ViewpointS may facilitate serendipitous discovery in an unprecedented way. In this paper, we wish to further motivate this last conjecture by proposing to explore two main research directions that did not convey sufficient attention by previous researchers (in particular those active in recommender systems): 1. assessing brain states in order to understand and forecast serendipitous human learning events triggered by emotions; 2. enhancing collective wisdom, since Human-Computer Interactions do not occur today between a human and a single machine (or algorithm), but within a community of humans and machines that continuously update “knowledge” beyond the scene. Both directions (assessment of brain states, collective wisdom) are currently on separate ways; we propose to combine them within one unified approach called ViewpointS.
Human Activity Recognition from Wearable Sensor Data Using Self-Attention.

Human Activity Recognition from body-worn sensor data poses an inherent challenge in capturing spatial and temporal dependencies of time-series signals. In this regard, the existing recurrent or convolutional or their hybrid models for activity recognition struggle to capture spatio-temporal context from the feature space of sensor reading sequence. To address this complex problem, we propose a self-attention based neural network model that foregoes recurrent architectures and utilizes different types of attention mechanisms to generate higher dimensional feature representation used for classification. We performed extensive experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-subject-out evaluation. We also observe that the sensor attention maps produced by our model is able capture the importance of the modality and placement of the sensors in predicting the different activity classes.
Generating Random Logic Programs Using Constraint Programming

Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.
Constructing Convolutional Neural Networks Based on Quaternion

A convolutional neural network based on quaternion, a four-dimensional hypercomplex number system, is proposed and evaluated in this paper. Called Quaternionic Convolutional Neural Networks (QCNNs), these networks can accept and operate three-dimensional signals by neurons in the networks. The performances of the proposed networks are investigated through classification of CIFAR-10 color images, and it is shown that the proposed QCNN outperforms a conventional (real-valued) CNN.
Temporal Difference Uncertainties as a Signal for Exploration

An effective approach to exploration in reinforcement learning is to rely on an agent's uncertainty over the optimal policy, which can yield near-optimal exploration strategies in tabular settings. However, in non-tabular settings that involve function approximators, obtaining accurate uncertainty estimates is almost as challenging a problem. In this paper, we highlight that value estimates are easily biased and temporally inconsistent. In light of this, we propose a novel method for estimating uncertainty over the value function that relies on inducing a distribution over temporal difference errors. This exploration signal controls for state-action transitions so as to isolate uncertainty in value that is due to uncertainty over the agent's parameters. Because our measure of uncertainty conditions on state-action transitions, we cannot act on this measure directly. Instead, we incorporate it as an intrinsic reward and treat exploration as a separate learning problem, induced by the agent's temporal difference uncertainties. We introduce a distinct exploration policy that learns to collect data with high estimated uncertainty, which gives rise to a curriculum that smoothly changes throughout learning and vanishes in the limit of perfect value estimates. We evaluate our method on hard exploration tasks, including Deep Sea and Atari 2600 environments and find that our proposed form of exploration facilitates both diverse and deep exploration.
The Cone Model: Recognizing gaze uncertainty in virtual environments

Eye tracking is used by psychologists, neurologists, vision researchers, and many others to understand the nuances of the human visual system, and to provide insight into a person’s allocation of attention across the visual environment. When tracking the gaze behavior of an observer immersed in a virtual environment displayed on a head-mounted display, estimated gaze direction is encoded as a three-dimensional vector extending from the estimated location of the eyes into the 3D virtual environment. Additional computation is required to detect the target object at which gaze was directed. These methods must be robust to calibration error or eye tracker noise, which may cause the gaze vector to miss the target object and hit an incorrect object at a different distance. Thus, the straightforward solution involving a single vector-to-object collision could be inaccurate in indicating object gaze. More involved metrics that rely upon an estimation of the angular distance from the ray to the center of the object must account for an object’s angular size based on distance, or irregularly shaped edges - information that is not made readily available by popular game engines (e.g. Unity© /Unreal© ) or rendering pipelines (OpenGL). The approach presented here avoids this limitation by projecting many rays distributed across an angular space that is centered upon the estimated gaze direction.
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning

We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this feature-space is far more effective than the standard meta-learning approaches. We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training. We show that our method of combining structured decoding with nearest neighbor learning achieves state-of-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by $6\%$ to $16\%$ absolute points over prior meta-learning based systems.
Remote education development trends during pandemic in Russia

Currently, the higher education system in Russia is in the midst of rapid changes associated with the fundamental problems of organizing the educational process during a pandemic. The pandemic of the new coronavirus infection COVID-19 in 2020 affected the educational system around the world, causing the massive closure of higher education institutions. Among the management practices of Russian universities related to the provision of security for students and staff, key decisions and recommendations based on a large-scale transition to the use of e-learning, distance learning technologies and the transition to a distance learning format, which has changed the usual way of life of university students and teachers. The objective of this research is to evaluate the transition of the educational process to distance education in the context of the pandemic, to identify the factors that hinder the development of learning. The research method is a questionnaire survey on distance learning using distance technologies in higher education. The results indicate that the development of a new model of education in universities can find resistance to external challenges and to a more productive way for economic and social development.
Indonesian traffic sign detection based on Haar-PHOG features and SVM classification

Abstract Segmentation and feature extraction contributes to improved accuracy in traffic sign detection. As traffic signs are often located in complex environments, it is essential to develop feature extraction based on shapes. The Haar–PHOG feature is a development of both HOG and PHOG based on Canny edge detection. One of its advantages is that PHOG feature conducts calculation in four different frequencies of LL, HL, LH, and HH. Results from experiments on four roads in Central Java and Yogyakarta using SVM classification show that the use of the Haar–PHOG feature provides a better result than the use of HOG and PHOG.
An Analysis of Text Difficulty across Grades in Korean Middle School English Textbooks Using Coh-Metrix

This study aims to investigate the text difficulty of the reading materials of Korean middle school English textbooks with Coh-Metrix, a software developed by the Institute for Intelligent Systems at the University of Memphis to analyze the linguistic and psycholinguistic features of English text and textbooks with a wide range of indices on cohesion and language.In this study, the textbook corpus consisted of the text files extracted from 13 English textbooks.These files were used for analyzing the text difficulty among grades with Coh-Metrix.The Coh-Metrix indices selected for this study contained basic counts, word frequency, word features, lexical diversity, pronouns, connectives, readability, syntax complexity, syntax similarity, reference cohesion, semantic cohesion, and situation model measures.The results showed that there were significant differences among grades for basic counts, word features, first pronouns, causal and temporal connectives, readability, reference and semantic cohesion, the number of words before main verbs, syntactic similarity, and situation model measures.The differences among grades, however, were not significant for word frequency, lexical diversity, second and third person pronouns, additive connectives, and NP density measures.The findings have educational implications for textbook design and language learning for English learners.
Analysis of Machine and Deep Learning Approaches for Credit Card Fraud Detection

In modern days, digitalization increased more demand because of faultless, ease, and convenient use of payment online. More people are choosing to pay the money through online mode through a safe gateway in e-commerce or e-trade. Today’s reality seems we are on the fast-growing to a cashless society. As indicated by the World Bank Report in the year of 2018 most of transactions are non-cash and also increased to 25%. Because of so many banking and financial companies spending more money to develop a application based on current demand. False transactions can happen in different manners and can be placed into various classifications. Learning approaches to classification play an essential role in detecting credit card fraud detection through online mode. There will be two significant reasons for the challenges of credit card detection. In the first challenge as the usage of the card has normal behavior or any fraudulent and second as most of the datasets are misrepresented for challenging to classify. In this paper, we investigate the machine and deep learning approaches usage of credit card fraud detection and other related papers and that merits and demerits and, of course, discussed challenges and opportunities.
Plug and Play Autoencoders for Conditional Text Generation

Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb). This reduces the need for labeled training data for the task and makes the training procedure more efficient. Crucial to the success of this method is a loss term for keeping the mapped embedding on the manifold of the autoencoder and a mapping which is trained to navigate the manifold by learning offset vectors. Evaluations on style transfer tasks both with and without sequence-to-sequence supervision show that our method performs better than or comparable to strong baselines while being up to four times faster.
Optimal noise estimation from syndrome statistics of quantum codes

Quantum error correction allows to actively correct errors occurring in a quantum computation when the noise is weak enough. To make this error correction competitive information about the specific noise is required. Traditionally, this information is obtained by benchmarking the device before operation. We address the question of what can be learned from only the measurements done during decoding. Such estimation of noise models was proposed for surface codes, exploiting their special structure, and in the limit of low error rates also for other codes. However, so far it has been unclear under what general conditions noise models can be estimated from the syndrome measurements. In this work, we derive a general condition for identifiability of the error rates. For general stabilizer codes, we prove identifiability under the assumption that the rates are small enough. Without this assumption we prove a result for perfect codes. Finally, we propose a practical estimation method with linear runtime for concatenated codes. We demonstrate that it outperforms other recently proposed methods and that the estimation is optimal in the sense that it reaches the Cram\'{e}r-Rao Bound. Our method paves the way for practical calibration of error corrected quantum devices during operation.
Stance Detection with Stance-Wise Convolution Network

Stance detection aims at identifying the stance (favor, against or neutral) of a text towards a specific target of opinion. Recently, there is a growing interest in using neural models for stance detection, but there are still some challenges to be solved. Firstly, it is difficult to associate text with target because targets are not always discussed explicitly in texts. However, existing methods always roughly model the representations of text and target on task-specific and limited corpus without considering the indispensable external information. Secondly, different from categories in normal classification task, we find that stances in stance detection task are not independent to each other. We study this observation and find it would be more effective to learn each stance individually. But all previous approaches ignore the correlation. To address these two challenges effectively, we introduce a Stance-wise Convolution Network (SCN) including two novel modules. Specifically, we first use a Text-Target Encoder module to subtly incorporate the pre-trained BERT into our model to learn more reasonable text-target representations. Then we propose a Stance-wise Convolution module to better learn stances by absorbing the correlation between stances. We evaluate our method on real-world dataset and the experimental results show that our proposed method achieves the state-of-the-art performance.
Student’s Perception of Online Learning for Public Speaking Course

This research is aimed to find the perception of students on the speaking ability in Public Speaking class during pandemic Covid-19. By communicating to students who are from Public Relation majored, the researchers considered that speaking as a tool of communication should be done intensively. Therefore, within this pandemic teachers and students should change their learning methods from offline face to face to online face to face. The authors stated the problems that the students faced, they are perceptions and difficulties of online public speaking learning. A qualitative method had been used to analyze this case. The authors used the percentage of students as respondents to see the perceptions. The result of this research viewed that students disagree with the online Public Speaking Course. This amount consisted of 35,4% of total respondents that possessed in the second rank. While the first rank with the amount of 36,5% stated hesitation whether should be conducted online or offline. This also made authors and may other researchers to rethink about online Public Speaking Course.&#x0D; &#x0D; Perception, Public Speaking, Online&#x0D;
Gate-Fusion Transformer for Multimodal Sentiment Analysis

Computational analysis of human multimodal sentiment is an emerging research area. Fusing semantic, visual and acoustic modalities requires exploring the inter-modal and intra-modal interactions. The first challenge for the inter-modal understanding is to break the heterogeneous gap between different modalities. Meanwhile, when modeling the intra-modal connection of time-series data, we must deal with the long-range dependencies among multiple steps. Moreover, The time-series data usually is unaligned between different modalities because individually specialized processing approaches or sampling frequencies. In this paper, we propose a method based on the transformer and the gate mechanism - the Gate-Fusion Transformer - to address these problems. We conducted detailed experiments for verifying the effectiveness of our proposed method. Because of the flexibility of gate-mechanism for information flow controlling and the great modeling power of the transformer for modeling the inter- and intra-modal interactions, we can achieve superior performance compared with the current state-of-the-art method but more extendible and flexible by stacking multiple gate-fusion blocks.
Crowd Emotion Analysis Using 2D ConvNets

Timely detection of the crowd emotions can lead to an effective crowd management measure. Crowd can exhibit multiple behaviour having separate emotions underneath it. Accurate emotion analysis of the crowd from a crowded image plays a critical role in the prudent examination of the crowded area. A popular non-intrusive solution for crowd behavior analysis is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. Detection of emotion of crowds becomes more pertinent in case of religious and political rallies. Peaceful conduct of these events is important for saving of human lives. The paper presents a novel method of deciphering crowd emotions using 2D convolutional neural network (ConvNets). This framework is then used to predict the crowd emotions, which leads to the subtle hints about the overall behaviour of crowd. The paper presents a suitable classifier for crowd emotion using the self-curated datasets for proving the concept that emotions can be extracted using ConvNets. Experiments have verified the proposed scheme on crowd behavior benchmark with fair accuracy.
The Authoritarian Assault on Knowledge

An urgent new class of challenges to independent intellectual inquiry has emerged on the global stage. These challenges stem primarily from the vulnerabilities that economic and technological change have introduced into the knowledge sectors of open societies, including both mature and developing democracies. In recent years, intensifying marketization has placed ever-greater financial and competitive pressures on publishers, universities, and other knowledge-sector institutions critical to the functioning of democracies. While a number of authoritarian regimes are investing in efforts to coerce and coopt knowledge-sector institutions in democracies, Beijing’s global reach stands out. Research institutions in democracies are responding by strengthening their internal processes, motivated by the threats of damaged reputations, loss of state funding, and stern regulatory or legislative action.