Reviewerly/exHarmony huggingface dataset:
Классификация текстов по жанрам на основе ритмических характеристик

The article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are markers of the text rhythm. On their basis, statistical features were calculated that describe quantitatively and structurally these rhythm features. The resulting text model was visualized for statistical analysis using boxplots and heat maps that showed differences in the rhythm of texts of different genres. The boxplots showed that almost all genres differ from each other in terms of the overall density of rhythm features. Heatmaps showed different rhythm patterns across genres. Further, the rhythm features were successfully used to classify texts into six genres. The classification was carried out in two ways: a binary classification for each genre in order to separate a particular genre from the rest genres, and a multi-class classification of the text corpus into six genres at once. Two text corpora in English and Russian were used for the experiments. Each corpus contains 100 fiction novels, scientific articles, advertisements and tweets, 50 reviews and political articles, i.e. a total of 500 texts. The high quality of the classification with neural networks showed that rhythm features are a good marker for most genres, especially fiction. The experiments were carried out using the ProseRhythmDetector software tool for Russian and English languages. Text corpora contains 300 texts for each language.
When Is Generalizable Reinforcement Learning Tractable

Agents trained by reinforcement learning (RL) often fail to generalize beyond the environment they were trained in, even when presented with new scenarios that seem similar to the training environment. We study the query complexity required to train RL agents that generalize to multiple environments. Intuitively, tractable generalization is only possible when the environments are similar or close in some sense. To capture this, we introduce Weak Proximity, a natural structural condition that requires the environments to have highly similar transition and reward functions and share a policy providing optimal value. Despite such shared structure, we prove that tractable generalization is impossible in the worst case. This holds even when each individual environment can be efficiently solved to obtain an optimal linear policy, and when the agent possesses a generative model. Our lower bound applies to the more complex task of representation learning for the purpose of efficient generalization to multiple environments. On the positive side, we introduce Strong Proximity, a strengthened condition which we prove is sufficient for efficient generalization.
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks

It is well known that modern deep neural networks are powerful enough to memorize datasets even when the labels have been randomized. Recently, Vershynin (2020) settled a long standing question by Baum (1988), proving that \emph{deep threshold} networks can memorize $n$ points in $d$ dimensions using $\widetilde{\mathcal{O}}(e^{1/\delta^2}+\sqrt{n})$ neurons and $\widetilde{\mathcal{O}}(e^{1/\delta^2}(d+\sqrt{n})+n)$ weights, where $\delta$ is the minimum distance between the points. In this work, we improve the dependence on $\delta$ from exponential to almost linear, proving that $\widetilde{\mathcal{O}}(\frac{1}{\delta}+\sqrt{n})$ neurons and $\widetilde{\mathcal{O}}(\frac{d}{\delta}+n)$ weights are sufficient. Our construction uses Gaussian random weights only in the first layer, while all the subsequent layers use binary or integer weights. We also prove new lower bounds by connecting memorization in neural networks to the purely geometric problem of separating $n$ points on a sphere using hyperplanes.
Abstract PO-014: VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts

Abstract Objective and quantifiable assessment of tissue pathology is necessary to study mechanistic disease progression; however, current quantification methods based on tissue staining have many drawbacks including cost, time, labor, batch effects, as well as uneven staining which can result in misinterpretation and investigator bias. Here we present VISTA, an automated deep learning tool for semantic segmentation and quantification of histologic features from hematoxylin and eosin (H&amp;E) stained pancreatic tissue sections. VISTA is trained to identify four key tissue types in developing murine PDAC samples: normal acinar, acinar-to-ductal metaplasia (ADM), dysplasia, and other normal tissue. Predicted segmentations were quantitatively evaluated against pathologist annotation with Dice Coefficients, achieving scores of 0.79, 0.70, 0.79 for normal acinar, ADM, and dysplasia, respectively. Predictions were evaluated against biological ground truth using the mean structural similarity index to immunostainings amylase and pan-keratin (0.925 and 0.920, respectively). The total area of feature prediction was also correlated to the area of immunostaining in whole tissue sections using spearman correlation (0.86, 0.97, and 0.92 for DAPI, amylase, and cytokeratins, respectively). Importantly, our tool is not only able to predict staining information, but it is able to distinguish between ADM and dysplasia, which are not reliably distinguished with common immunostaining methods, showing VISTA’s potential to expand research beyond what is capable with current standards. As a use case example of VISTA, we quantified abundance of histologic features in murine cohorts with oncogenic Kras-driven disease. We observed stromal expansion, a reduction in normal acinar, and an increase in both ADM and dysplasia as the disease progresses, which matches known biology. Since VISTA is an automated algorithm, it can accelerate histological analysis and improve the consistency of quantification between laboratories and investigators. This work has been published in Nature Scientific Reports, and the code is available on github at https://github.com/GelatinFrogs/MicePan-Segmentation. Citation Format: Luke Ternes, Ge Huang, Christian Lanciault, Guillaume Thibault, Rachelle Riggers, Joe Gray, John Muschler, Young Hwan Chang. VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-014.
THE CORRELATION BETWEEN GRAMMAR MASTERY AND READING COMPREHENSION

The purpose of this study is to know whether there is a significant correlation between the students’ grammar mastery and their reading comprehension. The design of this study was quantitative method and the technique used in this study was correlational technique. The sample of this study was taken from 49 students of fourth semester of English Education Study Program at STKIP PGRI Pasuruan. The instruments used in the study were documentation of students’ grammar scores and used test for reading comprehension. In assessing the students’ reading comprehension, this study conducted reading comprehension test which was adapted from the reading comprehension test from Active Cheat For Perfect TOEFL 677 by William Gregory’s Book 15th edition. The reading comprehension test and grammar score were high validity and reliability based on standardized. The finding showed that the data scores of two variables already fulfilled the four assumptions. The result of Pearson’s Product moment correlation showed that  there is a significant correlation between the students’ grammar mastery and their reading comprehension. It is proved by the r (0.921), the Sig. (2-tailed) were 0.000 ≤  0.01 or 0.000 ≤  0.05. As a result, the null hypothesis (Ho) is rejected and alternative hypothesis (Ha) is accepted.
Casting the Same Sentiment Classification Problem

We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity. Among other things, our goal is to enable a more topic-agnostic sentiment classification. We study the problem using the Yelp business review dataset, demonstrating how sentiment data needs to be prepared for this task, and then carry out sequence pair classification using the BERT language model. In a series of experiments, we achieve an accuracy above 83% for category subsets across topics, and 89% on average.
Detecting Cross-Geographic Biases in Toxicity Modeling on Social Media.

Online social media platforms increasingly rely on Natural Language Processing (NLP) techniques to detect abusive content at scale in order to mitigate the harms it causes to their users. However, these techniques suffer from various sampling and association biases present in training data, often resulting in sub-par performance on content relevant to marginalized groups, potentially furthering disproportionate harms towards them. Studies on such biases so far have focused on only a handful of axes of disparities and subgroups that have annotations/lexicons available. Consequently, biases concerning non-Western contexts are largely ignored in the literature. In this paper, we introduce a weakly supervised method to robustly detect lexical biases in broader geo-cultural contexts. Through a case study on a publicly available toxicity detection model, we demonstrate that our method identifies salient groups of cross-geographic errors, and, in a follow up, demonstrate that these groupings reflect human judgments of offensive and inoffensive language in those geographic contexts. We also conduct analysis of a model trained on a dataset with ground truth labels to better understand these biases, and present preliminary mitigation experiments.
Automated video analysis for social science research1

Widespread digitization is a disruptive force for the social sciences by generating vast data on human behavior. While social scientists have made increasing use of the opportunities that result from the widespread availability of digitized text, scholars have only begun to tap the more general potentials of the universal digitization of data, specifically by analyzing digitized images, as well as audio and video data. To highlight some of these potentials, the chapter discusses the state of the art in automated video analyses. First, we provide an overview of technical solutions for common classification scenarios in video data, such as object detection, pose detection, activity recognition, and person identification. Next, we discuss some current studies that use audio and video data to study social science phenomena. In the third part of the chapter, we discuss a sample application on video footage from a German state-level parliament in greater detail. The chapter closes with a discussion of potentials and challenges for automated video analysis in social science research.
UTILIZING TECHNOLOGICAL AIDS IN ENGLISH LANGUAGE LEARNING

In this globalization era, the advancement of technology is growing rapidly day by day, unstoppable. Technology helps people to simplify their works. Many aspects of life have been positively affected by current technology. Technology becomes the normal and expected means of communications and education. In English Language Learning, the latest technology such as computers, the internet, electronic dictionary, email, blogs, mobile phones, online course may promote English use wider. Moreover, it is seen as the tools to promote autonomous learning. The emerging current technology creates new teaching model, in which technology-based activities are seen as more effective, efficient, engaging and enjoyable in classroom practices. This research is aimed to analyze the application of current media and elaborate challenges faced by utilizing these technologies. This study also raises teacher’s awareness in using them effectively. Trained teachers are needed to apply this new teaching model successfully. Most importantly, by integrating technological aids as classroom media will surely motivate students’ interests to learn and communicate English in their daily interactions.
BROTLI DATA COMPRESSION ON MOODLE-BASEDE-LEARNING SERVER

The limited capacity of media storage and the increased number and sizeof data every day could decrease the productivity level. These problems can be solvedby compression methods that have been commonly used. Compression makes data storagemore efficient and shortens the time of data exchange. The compression algorithmused in this study is Brotli. The algorithm was implemented in the Moodle-based UMNe-learning server as a Moodle plugin. The compression application was developed byusing Java programming language, and the Moodle plugin was developed by using PHPprogramming language. The test results on 33 files with various compression sizes gavean average compression ratio of 0.77 and the average compression factor of 1.707, withthe average compression saving percentage of 22.94%. Comparative tests with the GoogleBrotli apps show an average of 97.71%. The compression test was done by compressingdata using the created application and decompressing data using Google’s application.The compression test shows that each file was successfully decompressed using an applicationfrom Google. These results indicate that the Brotli compression algorithm hasbeen successfully implemented in this study.
Can Perceptual Guidance Lead to Semantically Explainable Adversarial Perturbations?

It is well known that carefully crafted imperceptible perturbations can cause state-of-the-art deep learning classification models to misclassify. Understanding and analyzing these adversarial perturbations play a crucial role in the design of robust convolutional neural networks. However, their mechanics are not well understood. In this work, we investigate the following question: Is there any relationship between the imperceptibility and semantic significance of adversarial perturbations? In other words, can adversarial perturbations change only the regions of the image that are important for classification, while still being imperceptible? To address this, we consider generating adversarial perturbations by explicitly incorporating a “perceptual quality ball” constraint in our formulation. We pose this adversarial example generation problem as a tractable convex optimization problem, with constraints taken from a mathematically amenable variant of the popular SSIM index. We also provide a fast approximation algorithm to solve the proposed convex optimization problem, bringing our technique in line with other <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{p}$</tex-math></inline-formula> based techniques like PGD, both in form and computational complexity. We use the MobileNetV2 network trained on the ImageNet dataset for our experiments. By comparing the SSIM maps generated by our method with class activation maps, we observe that the perceptually guided perturbations generated by the proposed technique introduce changes specifically in the regions that contribute to classification decisions i.e., the proposed method induces changes that are semantic.
Independent Prototype Propagation for Zero-Shot Compositionality

Humans are good at compositional zero-shot reasoning; someone who has never seen a zebra before could nevertheless recognize one when we tell them it looks like a horse with black and white stripes. Machine learning systems, on the other hand, usually leverage spurious correlations in the training data, and while such correlations can help recognize objects in context, they hurt generalization. To be able to deal with underspecified datasets while still leveraging contextual clues during classification, we propose ProtoProp, a novel prototype propagation graph method. First we learn prototypical representations of objects (e.g., zebra) that are conditionally independent w.r.t. their attribute labels (e.g., stripes) and vice versa. Next we propagate the independent prototypes through a compositional graph, to learn compositional prototypes of novel attribute-object combinations that reflect the dependencies of the target distribution. The method does not rely on any external data, such as class hierarchy graphs or pretrained word embeddings. We evaluate our approach on AO-Clever, a synthetic and strongly visual dataset with clean labels, and UT-Zappos, a noisy real-world dataset of fine-grained shoe types. We show that in the generalized compositional zero-shot setting we outperform state-of-the-art results, and through ablations we show the importance of each part of the method and their contribution to the final results.
Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos

We introduce the task of spatially localizing narrated interactions in videos. Key to our approach is the ability to learn to spatially localize interactions with self-supervision on a large corpus of videos with accompanying transcribed narrations. To achieve this goal, we propose a multilayer cross-modal attention network that enables effective optimization of a contrastive loss during training. We introduce a divided strategy that alternates between computing inter- and intra-modal attention across the visual and natural language modalities, which allows effective training via directly contrasting the two modalities' representations. We demonstrate the effectiveness of our approach by self-training on the HowTo100M instructional video dataset and evaluating on a newly collected dataset of localized described interactions in the YouCook2 dataset. We show that our approach outperforms alternative baselines, including shallow co-attention and full cross-modal attention. We also apply our approach to grounding phrases in images with weak supervision on Flickr30K and show that stacking multiple attention layers is effective and, when combined with a word-to-region loss, achieves state of the art on recall-at-one and pointing hand accuracies.
BALANCING RIGHTS AMIDST SOCIAL MEDIA SCANDALS: HOW EMPLOYERS CAN DEAL WITH EMPLOYEES’ REPUTATION-DAMAGING AND/OR DEFAMATORY SOCIAL MEDIA POSTS

It is unquestioned that social media is present in almost every aspect of our daily lives. Due to the widespread accessibility of posts on social media, comments posted by a person in his/her personal capacity often boils over and negatively affects his/her role as an employee. Posts have the potential to either damage the reputation of an organisation directly or indirectly (the latter being caused by an employee’s mere association with the business). Defamatory posts are becoming more and more common and, consequently, social media misconduct clashes are finding their way into dispute resolutions forums. These disputes create a constant battle between the rights of an employer and the rights of an employee. Given that social media misconduct can be rather complex, it is imperative that employers are aware of the legislation governing misconduct to ensure that they are well-prepared to take preventative or swift action should the need arise.
Optimal Rates for Random Order Online Optimization

We study online convex optimization in the random order model, recently proposed by \citet{garber2020online}, where the loss functions may be chosen by an adversary, but are then presented to the online algorithm in a uniformly random order. Focusing on the scenario where the cumulative loss function is (strongly) convex, yet individual loss functions are smooth but might be non-convex, we give algorithms that achieve the optimal bounds and significantly outperform the results of \citet{garber2020online}, completely removing the dimension dependence and improving their scaling with respect to the strong convexity parameter. Our analysis relies on novel connections between algorithmic stability and generalization for sampling without-replacement analogous to those studied in the with-replacement i.i.d.~setting, as well as on a refined average stability analysis of stochastic gradient descent.
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision

A recently proposed class of models attempts to learn latent dynamics from high-dimensional observations, like images, using priors informed by Hamiltonian mechanics. While these models have important potential applications in areas like robotics or autonomous driving, there is currently no good way to evaluate their performance: existing methods primarily rely on image reconstruction quality, which does not always reflect the quality of the learnt latent dynamics. In this work, we empirically highlight the problems with the existing measures and develop a set of new measures, including a binary indicator of whether the underlying Hamiltonian dynamics have been faithfully captured, which we call Symplecticity Metric or SyMetric. Our measures take advantage of the known properties of Hamiltonian dynamics and are more discriminative of the model's ability to capture the underlying dynamics than reconstruction error. Using SyMetric, we identify a set of architectural choices that significantly improve the performance of a previously proposed model for inferring latent dynamics from pixels, the Hamiltonian Generative Network (HGN). Unlike the original HGN, the new HGN++ is able to discover an interpretable phase space with physically meaningful latents on some datasets. Furthermore, it is stable for significantly longer rollouts on a diverse range of 13 datasets, producing rollouts of essentially infinite length both forward and backwards in time with no degradation in quality on a subset of the datasets.
Pedestrian Detection Method Based on Recurrent Convolutional Neural Networks

To improve the accuracy of pedestrian detection in videos, a pedestrian detection method based on a recurrent convolutional neural network was proposed. The method used the recurrent convolutional neural network to combine the context information among several consecutive images in the video for implementing accurate pedestrian detection. Firstly, a convolutional neural network was used to extract several groups of feature maps of corresponding input images. Secondly, according to the image order, these groups of feature maps were successively input to the recurrent convolutional neural network to form a mask indicating the pedestrian locations. Finally, the pedestrian detection result of the current image was obtained by estimating the bounding boxes on the mask. The experimental results showed that the proposed method can achieve the accurate pedestrian detection results on three datasets (ETH, CUHK, PETS 2007) when compared with other pedestrian detection methods.
Модель взаимодействия педагога и обучаемого на основе принципов сотрудничества с учетом психологических аспектов

The article considers the modeling of the process of interaction between a teacher and a student based on the principles of cooperation, taking into account psychological aspects. The interaction of teachers and students at the university should have a wide range of tools that ensure the development of skills and abilities of students. One of the most important points in this is the interaction that develops between the teacher and the student: entering into subjective relations and being an active participant in them, the student begins to perceive the implemented methods of communication as the norm, as his individual choice. Therefore, the purpose of the study was the interaction of the teacher of students within the educational process of the university on the basis of the principles of cooperation, taking into account psychological aspects.