Reviewerly/exHarmony huggingface dataset:
Effects of Curriculum in Pakistan at Primary Level

The study is aimed at the effects of the curriculum in Pakistan at the primary level.Following were the main objectives of this study, to recognize the central problems of the primary education system in Pakistan.This study is descriptive in nature of district Lahore.Methods and procedures research methodology.This research study attempts to explore the main causes of the problems of the primary education system in Pakistan.4 District Punjab.Sampling, the entire 100 students were as a sample of the study.Data will be collected and tabulated, interpreted, and analyzed in terms of the percentage.So, the conclusion will be drawn recommendations will for further research on this topic.The questionnaire was personally curriculum at the primary level by the researchers to all the respondents included in the sample.Because of this study, researchers would like to make the following findings.An understanding of the need for communication and transport as essential elements in development strongly agreed 90%, Agreed 60%, neutral 10% strongly disagreed 05% and disagree 07%.In light of the analysis of data and findings of the study, the following conclusions are drawn.To promote intellectual development of the child which includes literacy in languages, numeracy in Mathematics, inquiry in Science, problem-solving in Social Studies and inculcating values in Islamite strongly agreed 60%, Agreed 90%, neutral 20% strongly disagreed 10% and disagree 10%.Because of the findings and conclusions of this study, researchers would like to make the following recommendations.Every teacher possesses a vision.The same may be encouraged and promoted.
The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

As the number of devices connected to the Internet of Things (IoT) increases significantly, it leads to an exponential growth in the number of services that need to be processed and stored in the large-scale Cloud-based service repositories. An efficient service indexing model is critical for service retrieval and management of large-scale Cloud-based service repositories. The multilevel index model is the state-of-art service indexing model in recent years to improve service discovery and combination. This paper aims to optimize the model to consider the impact of unequal appearing probability of service retrieval request parameters and service input parameters on service retrieval and service addition operations. The least-used key selection method has been proposed to narrow the search scope of service retrieval and reduce its time. The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a discriminatively pretrained encoder followed by a multi-scale generative decoders where the latter explicitly estimate likelihood of the encoded features. Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting. Our experiments on the MVTec dataset show that CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by 1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source our code with fully reproducible experiments.
DISCOURSE AND SUBJECT IN ARGUMENTATIVE MOVEMENT ABOUT PLAYING AND PLAYING IN CHILDHOOD

Recognizing play as the main activity in childhood and also that the right to play has been excluded from school routines, this work aims to analyze the discourse produced by subject-students, aged between 6 and 7 years, the in order to understand how they argue and build meanings about playing and playing in childhood.The work has as its theoretical foundation the concepts of Michel Pêcheux's Discourse Analysis, to analyze the effects of meanings produced by the subjects, in addition to considering Plantin's studies to analyze the arguments regarding the way subjects talk about their emotions, valuing the affections that can echo in arguments about the right to play.
STUDENT ATTITUDE TOWARD CONNECTING STRATEGY IN READING

&#x0D; &#x0D; &#x0D; Reading is one of the most important thing in this whole world. Researchers have become increasingly interested in children’s motivation to read. Because reading is an exigent activity that involves choice, motivation is crucial to reading engagement. Besides, reading is not just for reading, but also to increase our knowledges about everything that is very important to everyone and ourselves to make a better world with reading, not just in mind but also for attitude. The purpose of this case study was to investigate student attitude toward connecting strategy in reading at the student in Department of English State Islamic University of North Sumatera in the third semester. It was hypothesized that students attitude would improve during their literacy in school and everywhere they doing their activities about reading. Students attitudes were assessed through an onlline interview using platform Google Form as one of the most common tools for doing miniresearch in this pandemic era of the Covid-19. Findings indicated that reading consistent involvement in the learning process was crucial for students success. Furthermore, students attitude toward connecting strategy were found to be multidimensioanl and challenging to assess.&#x0D; &#x0D; &#x0D;
A Novel Face Recognition Algorithm Based on Numerical value of Facial Feature Points

To improve the speed of querying the relevant information of the face by inputting the face image puts forward a face recognition method of numerical value based on the face facial feature points distance ratio. First, the face image is collected through the camera equipment and preprocessing image. Second, perform face detection and key point location on the image, and select some main key points. Third, calculate the distance between every two points, choose n distances from it that have more influence on the face difference, calculate the ratio of this n distance, and use it as a data feature of the human face. Comparison with the search time of the deep convolutional neural network of the face images set. It can see that extracting the seven feature points of the facial features and converting the face image into twenty-one-dimensional data can effectively promote the human face retrieval speed while ensuring accuracy.
When Blockchain Meets Smart Grids: A Comprehensive Survey

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. Compared with traditional centralized approaches, blockchain-based solutions are able to exploit the advantages of blockchain to realize better functionality in smart grids. However, the blockchain technology itself has its disadvantages in low processing throughput and weak privacy protection. Therefore, it is of paramount importance to study how to integrate blockchain with smart grids in a more effective way so that the advantages of blockchain can be maximized and its disadvantages can be avoided. This article surveys the state-of-the-art solutions aiming to integrate the emergent blockchain technology with smart grids. The goal of this survey is to discuss the necessity of applying blockchain in different components of smart grids, identify the challenges encountered by current solutions, and highlight the frameworks and techniques used to integrate blockchain with smart grids. We also present thorough comparison studies among blockchain-based solutions for smart grids from different perspectives, with the aim to provide insights on integrating blockchain with smart grids for different smart grid management tasks. Finally, we list the current projects and initiatives demonstrating the current effort from the practice side. Additionally, we draw attention to open problems that have not yet been tackled by existing solutions, and point out possible future research directions.
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks

The field of adversarial machine learning has experienced a near exponential growth in the amount of papers being produced since 2018. This massive information output has yet to be properly processed and categorized. In this paper, we seek to help alleviate this problem by systematizing the recent advances in adversarial machine learning black-box attacks since 2019. Our survey summarizes and categorizes 20 recent black-box attacks. We also present a new analysis for understanding the attack success rate with respect to the adversarial model used in each paper. Overall, our paper surveys a wide body of literature to highlight recent attack developments and organizes them into four attack categories: score based attacks, decision based attacks, transfer attacks and non-traditional attacks. Further, we provide a new mathematical framework to show exactly how attack results can fairly be compared.
A Fast Robust Numerical Continuation Solver to a Two-Dimensional Spectral Estimation Problem

This paper presents a fast algorithm to solve a spectral estimation problem for two-dimensional random fields. The latter is formulated as a convex optimization problem with the Itakura-Saito pseudodistance as the objective function subject to the constraints of moment equations. We exploit the structure of the Hessian of the dual objective function in order to make possible a fast Newton solver. Then we incorporate the Newton solver to a predictor-corrector numerical continuation method which is able to produce a parametrized family of solutions to the moment equations. We have performed two sets of numerical simulations to test our algorithm and spectral estimator. The simulations on the frequency estimation problem shows that our spectral estimator outperforms the classical windowed periodograms in the case of two hidden frequencies and has a higher resolution. The other set of simulations on system identification indicates that the numerical continuation method is more robust than Newton's method alone in ill-conditioned instances.
GANiry: Bald-to-Hairy Translation Using CycleGAN

This work presents our computer vision course project called bald men-to-hairy men translation using CycleGAN. On top of CycleGAN architecture, we utilize perceptual loss in order to achieve more realistic results. We also integrate conditional constrains to obtain different stylized and colored hairs on bald men. We conducted extensive experiments and present qualitative results in this paper. Our code and models are available at https://github.com/fidansamet/GANiry.
Stochastic batch size for adaptive regularization in deep network optimization

We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. The adaptive regularization is imposed by stochastic process in determining batch size for each model parameter at each optimization iteration. The stochastic batch size is determined by the update probability of each parameter following a distribution of gradient norms in consideration of their local and global properties in the neural network architecture where the range of gradient norms may vary within and across layers. We empirically demonstrate the effectiveness of our algorithm using an image classification task based on conventional network models applied to commonly used benchmark datasets. The quantitative evaluation indicates that our algorithm outperforms the state-of-the-art optimization algorithms in generalization while providing less sensitivity to the selection of batch size which often plays a critical role in optimization, thus achieving more robustness to the selection of regularity.
Adding Uncertainty to Neural Network Regression Tasks in the Geosciences

A simple method for adding uncertainty to neural network regression tasks via estimation of a general probability distribution is described. The methodology supports estimation of heteroscedastic, asymmetric uncertainties by a simple modification of the network output and loss function. Method performance is demonstrated with a simple one dimensional data set and then applied to a more complex regression task using synthetic climate data.
Toward Givenness Hierarchy Theoretic Natural Language Generation

Language-capable interactive robots participating in dialogues with human interlocutors must be able to naturally and efficiently communicate about the entities in their environment. A key aspect of such communication is the use of anaphoric language. The linguistic theory of the Givenness Hierarchy(GH) suggests that humans use anaphora based on the cognitive statuses their referents have in the minds of their interlocutors. In previous work, researchers presented GH-theoretic approaches to robot anaphora understanding. In this paper we describe how the GH might need to be used quite differently to facilitate robot anaphora generation.
Learning transferable and discriminative features for unsupervised domain adaptation

Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. Transferability and discriminability are two key criteria for characterizing the superiority of feature representations to enable successful domain adaptation. In this paper, a novel method called \textit{learning TransFerable and Discriminative Features for unsupervised domain adaptation} (TFDF) is proposed to optimize these two objectives simultaneously. On the one hand, distribution alignment is performed to reduce domain discrepancy and learn more transferable representations. Instead of adopting \textit{Maximum Mean Discrepancy} (MMD) which only captures the first-order statistical information to measure distribution discrepancy, we adopt a recently proposed statistic called \textit{Maximum Mean and Covariance Discrepancy} (MMCD), which can not only capture the first-order statistical information but also capture the second-order statistical information in the reproducing kernel Hilbert space (RKHS). On the other hand, we propose to explore both local discriminative information via manifold regularization and global discriminative information via minimizing the proposed \textit{class confusion} objective to learn more discriminative features, respectively. We integrate these two objectives into the \textit{Structural Risk Minimization} (RSM) framework and learn a domain-invariant classifier. Comprehensive experiments are conducted on five real-world datasets and the results verify the effectiveness of the proposed method.
Evaluation of Greek Word Embeddings

Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is of critical importance. Most research efforts are focusing on English word embeddings. This paper addresses the problem of constructing and evaluating such models for the Greek language. We created a new word analogy corpus considering the original English Word2vec word analogy corpus and some specific linguistic aspects of the Greek language as well. Moreover, we created a Greek version of WordSim353 corpora for a basic evaluation of word similarities. We tested seven word vector models and our evaluation showed that we are able to create meaningful representations. Last, we discovered that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.
Probabilistic Dependency Graphs

We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph.
Developing emotion recognition for video conference software to support people with autism

We develop an emotion recognition software for the use with a video conference software for autistic individuals which are unable to recognize emotions properly. It can get an image out of the video stream, detect the emotion in it with the help of a neural network and display the prediction to the user. The network is trained on facial landmark features. The software is fully modular to support adaption to different video conference software, programming languages and implementations.
Improving AI Text Recognition Accuracy with Enhanced OCR For Automated Guided Vehicle

this artificial intelligence robot uses a mini-computer to operate it and uses mechanical movement like a four-wheeled vehicle with a 2WD drive system. In this article, a control strategy of the AGV robot will be shown and implemented to detect the location. This research Uses OCR (Optical Character Recognition) for the OpenCV library itself which has been enhanced/modified. This enhanced OCR is the main library used in text recognition. This research produces very accurate text detection compared to the default OCR that was previously used on the AGV robot in our university. After the process of reading this text is passed, it will produce text previously read through the camera which will then provide output in the form of text where the AGV robot is located. After the reading is validated, the AGV robot will move to the next point until it returns to its starting point. Based on hardware implementation through testing in the AGV laboratory with artificial intelligence, it can work according to the algorithm and minimize reading errors with a 95% success rate.&#x0D;
Learning Directed Graphical Models from Gaussian Data

In this paper, we introduce a new directed graphical model from Gaussian data: the Gaussian graphical interaction model (GGIM). The development of this model comes from considering stationary Gaussian processes on graphs, and leveraging the equations between the resulting steady-state covariance matrix and the Laplacian matrix representing the interaction graph. Through the presentation of conceptually straightforward theory, we develop the new model and provide interpretations of the edges in the graphical model in terms of statistical measures. We show that when restricted to undirected graphs, the Laplacian matrix representing a GGIM is equivalent to the standard inverse covariance matrix that encodes conditional dependence relationships. Furthermore, our approach leads to a natural definition of directed conditional independence of two elements in a stationary Gaussian process. We demonstrate that the problem of learning sparse GGIMs for a given observation set can be framed as a LASSO problem. By comparison with the problem of inverse covariance estimation, we prove a bound on the difference between the covariance matrix corresponding to a sparse GGIM and the covariance matrix corresponding to the $l_1$-norm penalized maximum log-likelihood estimate. Finally, we consider the problem of learning GGIMs associated with sparse directed conditional dependence relationships. In all, the new model presents a novel perspective on directed relationships between variables and significantly expands on the state of the art in Gaussian graphical modeling.
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings

The number of senses of a given word, or polysemy, is a very subjective notion, which varies widely across annotators and resources. We propose a novel method to estimate polysemy, based on simple geometry in the contextual embedding space. Our approach is fully unsupervised and purely data-driven. We show through rigorous experiments that our rankings are well correlated (with strong statistical significance) with 6 different rankings derived from famous human-constructed resources such as WordNet, OntoNotes, Oxford, Wikipedia etc., for 6 different standard metrics. We also visualize and analyze the correlation between the human rankings. A valuable by-product of our method is the ability to sample, at no extra cost, sentences containing different senses of a given word. Finally, the fully unsupervised nature of our method makes it applicable to any language. Code and data are publicly available at https://github.com/ksipos/polysemy-assessment . The paper was accepted as a long paper at EACL 2021.
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning

Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward function limits this repertoire and imposes a manual engineering burden. Self-supervised agents that set their own goals can automate this process, but designing appropriate goal setting objectives can be difficult, and often involves heuristic design decisions. In this paper, we propose a formal exploration objective for goal-reaching policies that maximizes state coverage. We show that this objective is equivalent to maximizing goal reaching performance together with the entropy of the goal distribution, where goals correspond to full state observations. To instantiate this principle, we present an algorithm called Skew-Fit for learning a maximum-entropy goal distributions. We prove that, under regularity conditions, Skew-Fit converges to a uniform distribution over the set of valid states, even when we do not know this set beforehand. Our experiments show that combining Skew-Fit for learning goal distributions with existing goal-reaching methods outperforms a variety of prior methods on open-sourced visual goal-reaching tasks. Moreover, we demonstrate that Skew-Fit enables a real-world robot to learn to open a door, entirely from scratch, from pixels, and without any manually-designed reward function.