Reviewerly/exHarmony huggingface dataset:
Students’ Perceptions on the Use of Story Maps in Reading Comprehension

In the classroom, students need to have good reading comprehension. Without that, they will not have critical skills for studying academic material. However, many students in Indonesia still have difficulty understanding the text. Using an appropriate strategy can help them understand the text. Story maps can be used can be used for students to help them in understanding the text. This research aims to know students’ perceptions on the use of story maps in reading comprehension, the advantages and the difficulties of using story maps for students’ reading comprehension in class. This research was conducted to the students of tenth grade at one senior high school in Lamongan. The instruments in this study were a questionnaire and interview. The research design of this study used a qualitative research design. The technique to analyze the data were data reduction, data display, and conclusion drawing. The results show that story maps can help the students in understanding the text. Most students felt that they have an improvement in reading comprehension because they felt easy and fast to understand the text. Story maps also help the students understand the structure of the text and it was also an interesting activity. Besides that, the students also faced difficulties when using story maps such as difficult making the story maps, still don’t understand the concept of story maps and difficult to understand the content of the text so they also have difficulty in making story maps.
Trainable Ranking Models to Evaluate the Semantic Accuracy of Data-to-Text Neural Generator

In this paper, we introduce a new embeddingbased metric relying on trainable ranking models to evaluate the semantic accuracy of neural data-to-text generators.This metric is especially well suited to semantically and factually assess the performance of a text generator when tables can be associated with multiple references and table values contain textual utterances.We first present how one can implement and further specialize the metric by training the underlying ranking models on a legal Data-to-Text dataset.We show how it may provide a more robust evaluation than other evaluation schemes in challenging settings using a dataset comprising paraphrases between the table values and their respective references.Finally, we evaluate its generalization capabilities on a well-known dataset, WebNLG, by comparing it with human evaluation and a metric recently introduced based on natural language inference.We then illustrate how it naturally characterizes, both quantitatively and qualitatively, omissions and hallucinations.
Main trends of development of Russian ATD: the present and the future

Even though the wave of territorial reforms that swept through Russia in 2003-2008 is over, the relevance of this problem in our days has not disappeared.It would seem that in the last ten years, since the last reform, nothing interesting has happened in the sphere of administrative-territorial division, an opinion was created that the consolidation of the regions once and for all solved the issue of optimization of the subjects of the Federation and, thus, stabilized the net of administrative-territorial units for a long time.However, in fact, many problems have not been solved.Instead, there was only their "freeze", postponing their permission indefinitely.Based on the above-mentioned fact, it is safe to say that someday the unsolved problems will surface, and then the authorities will be forced to return to the reform in the regions, but it will happen likely in complicated conditions.The purpose of this work is to identify trends and directions of development of modern Russian administrative-territorial division (including in the relatively distant future in terms of a forecast), arguing pros and cons and giving proposals of changing them.In the author's opinion the scientific novelty of the research consists of the development of the system of Russian administrative-territorial division in the future.In addition, the author proposes his own version of the draft of its reform, based primarily on the principle of expediency of the separation of certain regions in the Soviet period, which have since retained the configuration of the territorial structure.The importance for the applied activity is to identify the imperfections of the administrative-territorial division, the consideration of which will make it possible to develop a more competent regional reform.
Robust Retrieval Augmented Generation for Zero-shot Slot Filling

Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI. One way to make headway for this problem is through advancements in a related task known as slot filling. In this task, given an entity query in form of [Entity, Slot, ?], a system is asked to ‘fill’ the slot by generating or extracting the missing value exploiting evidence extracted from relevant passage(s) in the given document collection. The recent works in the field try to solve this task in an end-to-end fashion using retrieval-based language models. In this paper, we present a novel approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. Our model reports large improvements on both T-REx and zsRE slot filling datasets, improving both passage retrieval and slot value generation, and ranking at the top-1 position in the KILT leaderboard. Moreover, we demonstrate the robustness of our system showing its domain adaptation capability on a new variant of the TACRED dataset for slot filling, through a combination of zero/few-shot learning. We release the source code and pre-trained models.
Simultaneous Semiparametric Estimation of Clustering and Regression

We investigate the parameter estimation of regression models with fixed group effects, when the group variable is missing while group related variables are available. This problem involves clustering to infer the missing group variable based on the group related variables, and regression to build a model on the target variable given the group and eventually additional variables. Thus, this problem can be formulated as the joint distribution modeling of the target and of the group related variables. The usual parameter estimation strategy for this joint model is a two-step approach starting by learning the group variable (clustering step) and then plugging in its estimator for fitting the regression model (regression step). However, this approach is suboptimal (providing in particular biased regression estimates) since it does not make use of the target variable for clustering. Thus, we claim for a simultaneous estimation approach of both clustering and regression, in a semi-parametric framework. Numerical experiments illustrate the benefits of our proposition by considering wide ranges of distributions and regression models. The relevance of our new method is illustrated on real data dealing with problems associated with high blood pressure prevention.
Monetary Policy Convergence within the EAC: A Focus on Interest Rate and Exchange Rate

This study was conducted to establish whether the key variables in monetary policy transmission mechanisms are converging within the East African Community. This region is eyeing having an economic union and subsequently a monetary union hence the significance of investing developments in the monetary sector. The analysis used panel data from the year 2005 to 2020 for five EACs. To test for convergence of interest rates and exchange rates, the analysis employed; unit-root test, sigma convergence, co-integration tests, and finally used the panel fixed effect model to establish the impact of the two variables on the GDP. The analysis shows that in the short run, there is no convergence in interest rates but there is convergence in exchange rates. However, in the long run, the two monetary policy variables are co-integrated indicating that the region is doing well in terms of integration in the financial sector in their preparation to form a common trade area and monetary union. The analysis of the impact of the two variables on economic growth shows that only the exchange rate is significant, therefore, the region should strive to foster a stable exchange rate regime to realize increased economic growth.
THE IMPORTANCE OF GRAPHIC ILLUSTRATIONS IN DEVELOPING COMMUNICATION SKILLS FOR AN AUTISTIC CHILD

Autism is derived from two Greek words (aut), meaning “self”, and “ism”, meaning “status”. This word is used to describe an unusually introvert person, meaning isolation and seclusion. Children with autism refuse to deal with others in addition to their different behaviours, from one child to another, where they suffer in one way or another from difficulties in developing and maintaining relationships with others. Autism spectrum indicates that there is a wide variation in autism behaviour as it is in the form of a spectrum that extends from mild to severe cases and doctors often describe the disease as a widespread development disorder which means that it affects all aspects of a child`s daily life
Histology of Mice (Mus Musculus) Liver Tissue Fixed with Carnoy's Solution With Variation of 4 Hours, 8 Hours and 12 Hours

Histopathological examination is a routine examination process for each tissue in the anatomic pathology laboratory. The stages in making histology preparations are fixation stages. The type of solution that can be used as a fixative solution other than 10% NBF is Carnoy's solution. Carnoy's solution is a fixative solution with a relatively fast fixation process of about 1-4 hours. The advantages of Carnoy's solution can lyse erythrocytes and dissolve lipids, have the ability to maintain cell nuclei, and retain glycogen. The purpose of this study was to determine the histology of the liver tissue preparations of mice (Mus Musculus) which were fixed using Carnoy's solution with time variations of 4 hours, 8 hours, and 12 hours. This is an observational research with descriptive research design criteria. The results of the histology of the liver tissue preparations of mice (Mus Musculus) which were fixed using the 10% NBF solution group obtained 100% good preparations. In the Carnoy solution group with a time of 4 hours, it gave 2.2% results of poor preparations and 97.78% of good preparations. While the treatment group with Carnoy's solution with a time of 8 hours obtained as many as 11.11% of preparations that were not good and 88.89% of good preparations. and in the Carnoy solution group with a time of 12 hours, there were 44.44% of the poor preparations and 55.56% of the good preparations.
Improving the Students’ Self-Confidence and Speaking Ability in the BIPA Class of Universitas PGRI Semarang through Video-Making Projects

This study aims to improve student confidence and speaking skills in the BIPA University PGRI Semarang class through video making projects. This research is a qualitative descriptive study. The data were provided by interview, observation, and documentation. The data analyzed is carried out by collecting data, coding data, comparing data, constructing interpretations, and reporting results. In addition, to overcome students 'problems, several attempts were made to increase students' confidence and speaking ability before making videos, such as doing role plays and simulations in class. After that, the students practice their speaking skills in real situations. The first cycle is making a video project around the City of Semarang, while the second cycle is carried out in the City of Magelang. This research data is presented by formal and informal methods. The results of the data analysis showed that the video making project was able to 1) significantly increase the student confidence and pronunciation; 2) improve the students' Indonesian language skills, especially fluency, vocabulary, and accuracy in grammar; and 3) increase their motivation and enthusiasm that encourage them to learn Indonesian in BIPA classes well.
Integrated approach to the analysis of the text of a work of fiction at the lessons of Foreign Literature

The current problems of professional competence of literary texts analysis development of prospective Foreign Literature teachers on the background of an integrated approach are considered in the given study. It is established that the introduction of this approach is carried out in the process of developing students’ personal image of each lesson topic in accordance with the school curriculum, expanding students’ associative links, their ability to interact with cultural images of different eras and countries, developing emotional and value-based attitude to the world and their place in it. The aim of the research is to develop the technique of an integrated approach to the analysis of a literary text taking into account aesthetic, culturological, and psychological backgrounds of perception of a literary text as a fiction text, age peculiarities of students’ understanding and interpretation of literary works. In the process of the given study it is found out that the introduction of the idea of an integrated approach to the text analysis in school encourages the teacher to go beyond their subject, when the teacher is able to comprehend the material from philosophical, literary, art, and cultural standpoints, aware of the place of their subject in culture; in the course of the analysis of the literary text the teacher uses various possibilities of interaction of literature with other kinds of art to provide direct participation of pupils in the independent analysis of the literary text and the organization of their art and creative activity at the lesson. The range of opening tasks that the teacher must perform in the course of preparation for such a literature lesson is defined. On the background of the analysis of the experience of teachers-practitioners, students’ answers and their creative work it is concluded that the creative activity of teachers and students within an integrated approach to the text analysis cannot be limited by mentioned activities in the classroom but it should be closely connected with extracurricular activities as well.
AutoEQA: Auto-Encoding Questions for Extractive Question Answering

There has been a significant progress in the field of extractive question answering (EQA) in the recent years.However, most of them rely on annotations of answer-spans in the corresponding passages.In this work, we address the problem of EQA when no annotations are present for the answer span, i.e., when the dataset contains only questions and corresponding passages.Our method is based on auto-encoding of the question that performs a question answering (QA) task during encoding and a question generation (QG) task during decoding.Our method performs well in a zeroshot setting and can provide an additional loss to boost performance for EQA.
Argumentation-Driven Evidence Association in Criminal Cases

Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction.Observably, evidence divided into the same subset usually supports the same claim.Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper.Experimental results on a real-world dataset demonstrate the effectiveness of our method.
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients

We consider a standard federated learning (FL) architecture where a group of clients periodically coordinate with a central server to train a statistical model. We develop a general algorithmic framework called FedLin to tackle some of the key challenges intrinsic to FL, namely objective heterogeneity, systems heterogeneity, and infrequent and imprecise communication. Our framework is motivated by the observation that under these challenges, various existing FL algorithms suffer from a fundamental speed-accuracy conflict: they either guarantee linear convergence but to an incorrect point, or convergence to the global minimum but at a sub-linear rate, i.e., fast convergence comes at the expense of accuracy. In contrast, when the clients' local loss functions are smooth and strongly convex, we show that FedLin guarantees linear convergence to the global minimum, despite arbitrary objective and systems heterogeneity. We then establish matching upper and lower bounds on the convergence rate of FedLin that highlight the effects of intermittent communication. Finally, we show that FedLin preserves linear convergence rates under aggressive gradient sparsification, and quantify the effect of the compression level on the convergence rate. Our work is the first to provide tight linear convergence rate guarantees, and constitutes the first comprehensive analysis of gradient sparsification in FL.
QUANTUM CODES FROM CYCLIC CODES OVER THE F_q+uF_q+vF_q+wF_q+uvF_q

In this paper, we give construction of quantum codes over F_q from cyclic codes over the F_q+uF_q+vF_q+wF_q+uvF_q, where u^2=u,v^2=v,w^2=w,uv=vu,uw=wu=vw=wv=0, q=p^m and p is an odd prime. We determine the parameters of quantum error correcting codes which constructed from cyclic codes over the F_q+uF_q+vF_q+wF_q+uvF_q. Finally, we give some examples of quantum codes.
Do Input Gradients Highlight Discriminative Features

Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. In this work, we test the validity of assumption (A) using a three-pronged approach. First, we develop an evaluation framework, DiffROAR, to test assumption (A) on four image classification benchmarks. Our results suggest that (i) input gradients of standard models (i.e., trained on original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models satisfy (A). Second, we then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models. Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. Specifically, we prove that input gradients of standard one-hidden-layer MLPs trained on this dataset do not highlight instance-specific signal coordinates, thus grossly violating assumption (A). Our findings motivate the need to formalize and test common assumptions in interpretability in a falsifiable manner [Leavitt and Morcos, 2020]. Additionally, we believe that the DiffROAR evaluation framework and BlockMNIST-based datasets can serve as sanity checks to audit instance-specific interpretability methods.
Image Caption Generator Tool Using CNN and LSTM

In this ever-growing social media generation, everyone posts different kinds of pictures of their own and surroundings in their social networking sites like Instagram, Twitter, Facebook, etc. People feel difficulty in finding quotes and captions for their posts and to make it easy we used some concepts of CNN and LSTM to read the picture and caption it as possible.Herewe systematically analyze the deep neural network based image caption generation method. With an image as the input, the method can output a caption of the image by analyzing the image.
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese

High quality distributional models can capture lexical and semantic relations between words.Hence, researchers design various intrinsic tasks to test whether such relations are captured.However, most of the intrinsic tasks are designed for modern languages, and there is a lack of evaluation methods for distributional models of historical corpora.In this paper, we conducted BAHP: a benchmark of assessing word embeddings in Historical Portuguese, which contains four types of tests: analogy, similarity, outlier detection, and coherence.We examined word2vec models generated from two historical Portuguese corpora in these four test sets.The results demonstrate that our test sets are capable of measuring the quality of vector space models and can provide a holistic view of the model's ability to capture syntactic and semantic information.Furthermore, the methodology for the creation of our test sets can be easily extended to other historical languages.
Fuzzy Control of a Servomechanism: Practical Approach using Mamdani and Takagi- Sugeno Controllers

The main objective of this work is to propose two fuzzy controllers: one based on the Mamdani inference method and another controller based on the Takagi- Sugeno inference method, both will be designed for application in a position control system of a servomechanism. Some comparations between the methods mentioned above will be made with regard to the performance of the system in order to identify the advantages of the Takagi- Sugeno method in relation to the Mamdani method in the presence of disturbances and nonlinearities of the system. Some results of simulation and practical application are presented and results obtained showed that controllers based on Takagi- Sugeno method is more efficient than controllers based on Mamdani method for this specific application.
Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models

The paper describes a system for automatic summarization in English language of online news data that come from different non-English languages.The system is designed to be used in production environment for media monitoring.Automatic summarization can be very helpful in this domain when applied as a helper tool for journalists so that they can review just the important information from the news channels.However, like every software solution, the automatic summarization needs performance monitoring and assured safe environment for the clients.In media monitoring environment the most problematic features to be addressed are: the copyright issues, the factual consistency, the style of the text and the ethical norms in journalism.Thus, the main contribution of our present work is that the above mentioned characteristics are successfully monitored in neural automatic summarization models and improved with the help of validation, fact-preserving and fact-checking procedures.
RULE BASED APPROACH FOR DETECTION OF MOBILE RANKING FRAUD

The technology advancement mobile industry is growing rapidly at a rapid pace and millions of apps continue to be developed every day. These apps are available from Google Play and Apple's stores, although there are other stores. Companies are trying to get noticed by users as they market their apps. They manipulate apps using R3 fraud methods such as Rating, Rating, and Reviews. These R3 features will make the app more popular than apps with lower R3 features. To make apps last in the market, a fraud mechanism could be implemented on top of the R3 features. A model is developed to detect fraud using the rating, review, and rating of mobile apps. This study will compare the model to existing models like KNN classification, Rule-based approach, Hidden Markov Model. Ensemble decision tree is our proposed model. It has proven to be more prominent than other models with accuracy greater than 85%, and thus can detect fraud over apps with the greatest prominence.