Reviewerly/exHarmony huggingface dataset:
NarrationBot and InfoBot: A Hybrid System for Automated Video Description

Video accessibility is crucial for blind and low vision users for equitable engagements in education, employment, and entertainment. Despite the availability of professional and amateur services and tools, most human-generated descriptions are expensive and time consuming. Moreover, the rate of human-generated descriptions cannot match the speed of video production. To overcome the increasing gaps in video accessibility, we developed a hybrid system of two tools to 1) automatically generate descriptions for videos and 2) provide answers or additional descriptions in response to user queries on a video. Results from a mixed-methods study with 26 blind and low vision individuals show that our system significantly improved user comprehension and enjoyment of selected videos when both tools were used in tandem. In addition, participants reported no significant difference in their ability to understand videos when presented with autogenerated descriptions versus human-revised autogenerated descriptions. Our results demonstrate user enthusiasm about the developed system and its promise for providing customized access to videos. We discuss the limitations of the current work and provide recommendations for the future development of automated video description tools.
OPTIMALISASI PERPUSTAKAAN RPTRA BERBASIS INKLUSI SOSIAL UNTUK MASYARAKAT KEPULAUAN SERIBU YANG MENGALAMI EKSKLUSI SOSIAL

Objective: 1) to identify the demography of the Kepulauan Seribu people who experienced social exclusion, 2) to know the role of the RPTRA library for Kepulauan Seribu community and 3) to formulate the optimized social inclusion RPTRAlibrary for Kepulauan Seribu community.Method: The research method is descriptive qualitative with a critical ethnographic approach. The data in this study are still in the form of preliminary data. Data collection carried out through 7 days of participant observation on UntungJawa Island and interviews. The technique of determining the informant was purposive sampling.Results: Kepulauan Seribu as a small archipelago with minimal transportation, means of communication made Kepulauan Seribu isolated from other regions. In addition, the poverty rate in the Thousand Islands shows the highest number compared to other districts / cities. While Human Development Index shows the lowest number. This shows that the people of the Thousand Islands experienced social exclusion. To overcome various social exclusion problems, the concept of social inclusion is a solution. One of the institutions that needs to pay attention on social inclusion is library. One facility that has a library is RPTRA. But so far the RPTRA library is still focused on children. Even though one of RPTRA's visions is to improve the economy of the community. Therefore, the RPTRA library needs to optimized to become a social inclusion based library to achieve community welfare and do the full vision of RPTRA.
Digital Evidence Exceptionalism? A Review and Discussion of Conceptual Hurdles in Digital Evidence Transformation

Forensic science is currently undergoing a transformation and expansion to include modern types of evidence, such as evidence generated by digital investigations. This development is said to raise a series of challenges, both in operational and conceptual dimensions. This paper reviews and discusses a series of convoluted conceptual hurdles that are encountered in connection with the use of digital evidence as part of evidence and proof processes at trial, in contradistinction to investigative uses of such types of evidence. As a recent example raising such hurdles, we analyse and discuss assertions and proposals made in the article “Digital Evidence Certainty Descriptors (DECDs)” by Graeme Horsman (32 Forensic Science International: Digital Investigation (2020) 200896).
EXPERIENTIAL OUTDOOR ACTIVITIES IN IMPROVING STUDENTS’ VOCABULARY ACHIEVEMENT OF THE EIGHT GRADE STUDENTS AT SMP NEGERI 4 SIPOHOLON IN ACADEMIC YEAR 2018/2019

This research is A Classroom Action Research held at SMP Negeri 4 Sipoholon in Academic Year 2018/2019. The problem of the research is; Does experiential outdoor activities significantly improve the students’ vocabulary achievement of the eight grade students at SMP Negeri 4 Sipoholon? And the objective is to find out whether experiential outdoor activities significantly improve the students’ vocabulary achievement. The subject of the research is 20 students of the eight grade students of SMP N. 4 Sipoholon in academic year 2018/2019. This research was only acted in one cycle in which, that in pre-test there was not student who got good and very good interpretation yet there are one who was fail and 8 students who were poor, in the minimum criterion used in the school is 70, so the researcher concluded that based on the students’ pre-test score they are fail. In fact based on the data analysis of the post-test that there are 11 students who got very good (80 – 100) and 9 students got good (66 – 79), and none of the students got fair (56-65), poor (40-55), and fail ( 55.25. In addition, p-value under df (degree of freedom) is 19 or 1.729, it was found t-value has a significant different with t-table or 78.25 > 1.729. On the other hand, it can be said that the Ho (null hypothesis) is rejected and the Ha (alternative hypothesis) is accepted, in which there is a significant improvement of students’ vocabulary achievement taught by experiential outdoor activities.
Detection of Fake Twitter Profiles using SVM, Random Forest and Neural Network Models

Online social networks are one of the most important parts of our lives. Today, online social media has changed the way we communicate. People are more likely to use social media to keep in touch with family, friends, and college. Social media creates a perfect environment for hackers. They use fake accounts on social media platforms such as Facebook, Twitter, Instagram to access personal data. The purpose of our research is to create a model that will detect whether an account is fake or not, thus protecting people from fake accounts and providing them with privacy. This is done taking into account some algorithms such as SVM, Random Forest Algorithm and neural network models.
Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope.

Abstract Meaning Representation (AMR) is a graphical meaning representation language designed to represent propositional information about argument structure. However, at present it is unable to satisfyingly represent non-veridical intensional contexts, often licensing inappropriate inferences. In this paper, we show how to resolve the problem of non-veridicality without appealing to layered graphs through a mapping from AMRs into Simply-Typed Lambda Calculus (STLC). At least for some cases, this requires the introduction of a new role :content which functions as an intensional operator. The translation proposed is inspired by the formal linguistics literature on the event semantics of attitude reports. Next, we address the interaction of quantifier scope and intensional operators in so-called de re/de dicto ambiguities. We adopt a scope node from the literature and provide an explicit multidimensional semantics utilizing Cooper storage which allows us to derive the de re and de dicto scope readings as well as intermediate scope readings which prove difficult for accounts without a scope node.
Психологические особенности педагогической деятельности и педагогического общения

The article examines the specific psychological aspects of the teacher's professional activity and pedagogical relationships
Truth-Conditional Captions for Time Series Data

In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week. A model for this task should be able to extract high-level patterns such as presence of a peak or a dip. While typical contemporary neural models with attention mechanisms can generate fluent output descriptions for this task, they often generate factually incorrect descriptions. We propose a computational model with a truth-conditional architecture which first runs small learned programs on the input time series, then identifies the programs/patterns which hold true for the given input, and finally conditions on *only* the chosen valid program (rather than the input time series) to generate the output text description. A program in our model is constructed from modules, which are small neural networks that are designed to capture numerical patterns and temporal information. The modules are shared across multiple programs, enabling compositionality as well as efficient learning of module parameters. The modules, as well as the composition of the modules, are unobserved in data, and we learn them in an end-to-end fashion with the only training signal coming from the accompanying natural language text descriptions. We find that the proposed model is able to generate high-precision captions even though we consider a small and simple space of module types.
La nueva educación en pandemia: espacio y nuevas tecnologías

espanolLa pandemia obliga a repensar nuestros modelos educativos y ofrece, de este modo, la oportunidad de integrar por fin lo fisico y lo virtual, la tradicion y las nuevas tecnologias. Se trata, a mi juicio, de una ocasion definitiva tambien para que los jovenes dejen de ver equivocadamente el arte como algo pasado y se interesen por el como lo hacen por otras disciplinas. Mi propuesta para modernizar la ensenanza de las artes y la literatura se asienta en dos pilares: transformar el espacio de aprendizaje en un lugar inspirador y utilizar las nuevas tecnologias, integradas en el, como herramienta para acercar el conocimiento de las artes y desarrollar asi su gusto por ellas EnglishThe pandemic obligues us to re-think our own educational patters but also is an opportunity for a better integration, at least, between virtual and physical world, tradition and new technologies. In my opinion, it´s the definitive occasion to stop young people seeing art like past and get their interest for it in the same way they are in another subjects. My proposal to modernise teaching of arts and literature is based in two pillars: to transform the learning space into an inspiring place and to use new technologies, integrated in it, as a tool to bring knowledge of the arts closer to young people and thus develop their taste for them.
Transformation on The Dynamics of Indian Educational System Through The Ages

Education policy has a hoary past.For a better understanding of education system in India it is divided into two major periods namely pre independence and post independence. The influence of this promotion the use of English education decision may still be seen in Indian education today. The country's educational system, had far-reaching consequences during the colonial period. India's independence opened a new chapter in the history of education policy. Indian society is extremely varied, which has led to a variety of issues and challenges.This paper portrays to demonstrates about education's goals, the tactics used to achieve these goals, and the instruments used to evaluate their impact.
On Linear Stability of SGD and Input-Smoothness of Neural Networks

The multiplicative structure of parameters and input data in the first layer of neural networks is explored to build connection between the landscape of the loss function with respect to parameters and the landscape of the model function with respect to input data. By this connection, it is shown that flat minima regularize the gradient of the model function, which explains the good generalization performance of flat minima. Then, we go beyond the flatness and consider high-order moments of the gradient noise, and show that Stochastic Gradient Descent (SGD) tends to impose constraints on these moments by a linear stability analysis of SGD around global minima. Together with the multiplicative structure, we identify the Sobolev regularization effect of SGD, i.e. SGD regularizes the Sobolev seminorms of the model function with respect to the input data. Finally, bounds for generalization error and adversarial robustness are provided for solutions found by SGD under assumptions of the data distribution.
Pricing Query Complexity of Revenue Maximization

The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most with the smallest amount of data? For truthful auctions, this is the sample complexity problem. For posted price auctions, we no longer have access to samples. Instead, the algorithm is allowed to choose a price pt; then for a fresh sample vt ~ D we learn the sign st = sign(pt — vt) ∈ {-1, +1}. How many pricing queries are needed to estimate a given parameter of the underlying distribution?We give tight upper and lower bounds on the number of pricing queries required to find an approximately optimal reserve price for general, regular and MHR distributions. Interestingly, for regular distributions, the pricing query and sample complexities match. But for general and MHR distributions, we show a strict separation between them.All known results on sample complexity for revenue optimization follow from a variant of using the optimal reserve price of the empirical distribution. In the pricing query complexity setting, we show that learning the entire distribution within an error of ε in Levy distance requires strictly more pricing queries than to estimate the reserve. Instead, our algorithm uses a new property we identify called relative flatness to quickly zoom into the right region of the distribution to get the optimal pricing query complexity.* The full version of the paper can be accessed at https://arxiv.org/abs/2111.03158
Implicit Generative Copulas

Copulas are a powerful tool for modeling multivariate distributions as they allow to separately estimate the univariate marginal distributions and the joint dependency structure. However, known parametric copulas offer limited flexibility especially in high dimensions, while commonly used non-parametric methods suffer from the curse of dimensionality. A popular remedy is to construct a tree-based hierarchy of conditional bivariate copulas. In this paper, we propose a flexible, yet conceptually simple alternative based on implicit generative neural networks. The key challenge is to ensure marginal uniformity of the estimated copula distribution. We achieve this by learning a multivariate latent distribution with unspecified marginals but the desired dependency structure. By applying the probability integral transform, we can then obtain samples from the high-dimensional copula distribution without relying on parametric assumptions or the need to find a suitable tree structure. Experiments on synthetic and real data from finance, physics, and image generation demonstrate the performance of this approach.
Can Information Flows Suggest Targets for Interventions in Neural Circuits

Motivated by neuroscientific and clinical applications, we empirically examine whether observational measures of information flow can suggest interventions. We do so by performing experiments on artificial neural networks in the context of fairness in machine learning, where the goal is to induce fairness in the system through interventions. Using our recently developed $M$-information flow framework, we measure the flow of information about the true label (responsible for accuracy, and hence desirable), and separately, the flow of information about a protected attribute (responsible for bias, and hence undesirable) on the edges of a trained neural network. We then compare the flow magnitudes against the effect of intervening on those edges by pruning. We show that pruning edges that carry larger information flows about the protected attribute reduces bias at the output to a greater extent. This demonstrates that $M$-information flow can meaningfully suggest targets for interventions, answering the title's question in the affirmative. We also evaluate bias-accuracy tradeoffs for different intervention strategies, to analyze how one might use estimates of desirable and undesirable information flows (here, accuracy and bias flows) to inform interventions that preserve the former while reducing the latter.
Analysis the Synonym 刚, 刚刚 and 刚才 on Based of Corpus

本文利用BCC语料库提供的统计功能和多种检索式, 探索辨析同义词‘刚’、‘刚刚’、‘刚才’的词频分布差异, 单词搭配差异和语义韵的差异等方面的特征的具体方法, 以助于恰当使用同义词, 进而更好的发挥汉语交际功能。 语料库语言学途径的同义词辨析主要通过考察同义词在不同语域中的词频分布差异。统计各搭配词与关键词共现的显著程度, 观察检索行中所呈现的同义词搭配特征, 揭示出它们不同的类联结、搭配关系和语义韵等语言特征。得益于巨大库容的语料库所提供的翔实的证据, 语料库索引可对同义词群提供丰富的用法和语境, 使研究者能够比较和掌握同义词之间细微的语义语用差异, 实现对同义词的细微差异进行客观和全面的描述。
Research on Image Style Transfer Technique Based on Normalization

In this paper, the existing Gram algorithm based on CNN network is improved to calculate the image features of mean standard deviation. In this method, the feature space is constructed to store the feature information of different filters, and different normalized statistics are realized in different network layers, so that multi-scale and stable features can be obtained better. It does not need to train the real data, and style transfer can be carried out flexibly. According to the theoretical analysis of CNN, the feature information of the high-level network reconstruction is constructed and extracted, and the FC layer and soft-max layer are removed to improve the operating efficiency. Experimental results show that the performance of the proposed mean-standard deviation algorithm is better than that of the Gram algorithm in the process of style transfer. Meanwhile, the distortion effect of style transfer is small, and the running efficiency is significantly improved.
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification

Semi-supervised learning aims to leverage a large amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. In this paper, we delve into semi-supervised learning for object detection, where labeled data are more labor-intensive to collect. Current methods are easily distracted by noisy regions generated by pseudo labels. To combat the noisy labeling, we propose noise-resistant semi-supervised learning by quantifying the region uncertainty. We first investigate the adverse effects brought by different forms of noise associated with pseudo labels. Then we propose to quantify the uncertainty of regions by identifying the noise-resistant properties of regions over different strengths. By importing the region uncertainty quantification and promoting multipeak probability distribution output, we introduce uncertainty into training and further achieve noise-resistant learning. Experiments on both PASCAL VOC and MS COCO demonstrate the extraordinary performance of our method.
Well-tuned Simple Nets Excel on Tabular Datasets.

Tabular datasets are the last unconquered castle for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures. In this paper, we hypothesize that the key to boosting the performance of neural networks lies in rethinking the joint and simultaneous application of a large set of modern regularization techniques. As a result, we propose regularizing plain Multilayer Perceptron (MLP) networks by searching for the optimal combination/cocktail of 13 regularization techniques for each dataset using a joint optimization over the decision on which regularizers to apply and their subsidiary hyperparameters. We empirically assess the impact of these regularization cocktails for MLPs in a large-scale empirical study comprising 40 tabular datasets and demonstrate that (i) well-regularized plain MLPs significantly outperform recent state-of-the-art specialized neural network architectures, and (ii) they even outperform strong traditional ML methods, such as XGBoost.
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning

We present and analyze an algorithm for optimizing smooth and convex or strongly convex objectives using minibatch stochastic gradient estimates. The algorithm is optimal with respect to its dependence on both the minibatch size and minimum expected loss simultaneously. This improves over the optimal method of Lan (2012), which is insensitive to the minimum expected loss; over the optimistic acceleration of Cotter et al. (2011), which has suboptimal dependence on the minibatch size; and over the algorithm of Liu and Belkin (2018), which is limited to least squares problems and is also similarly suboptimal with respect to the minibatch size. Applied to interpolation learning, the improvement over Cotter et al. and Liu and Belkin translates to a linear, rather than square-root, parallelization speedup.
Bandit Phase Retrieval

We study a bandit version of phase retrieval where the learner chooses actions $(A_t)_{t=1}^n$ in the $d$-dimensional unit ball and the expected reward is $\langle A_t, \theta_\star\rangle^2$ where $\theta_\star \in \mathbb R^d$ is an unknown parameter vector. We prove that the minimax cumulative regret in this problem is $\smash{\tilde \Theta(d \sqrt{n})}$, which improves on the best known bounds by a factor of $\smash{\sqrt{d}}$. We also show that the minimax simple regret is $\smash{\tilde \Theta(d / \sqrt{n})}$ and that this is only achievable by an adaptive algorithm. Our analysis shows that an apparently convincing heuristic for guessing lower bounds can be misleading and that uniform bounds on the information ratio for information-directed sampling are not sufficient for optimal regret.