Reviewerly/exHarmony huggingface dataset:
Evaluation Algorithm of Teaching Work Quality in Colleges and Universities Based on Deep Denoising Autoencoder Network

One of the most significant components of the teaching department’s evaluation of teaching quality is evaluating teachers’ performance. With the acceleration of educational informatization, modern information processing technology can be used effectively to evaluate teachers’ teaching quality in traditional teaching. In this context, combined with some computational intelligence algorithms, it is critical to developing a targeted teaching quality evaluation system. This paper studies teacher teaching evaluation’s characteristics and existing problems and analyzes the fundamental theories and methods of teacher teaching evaluation in colleges and universities. A novel combination of deep denoising autoencoder and support vector machine was proposed for evaluating teacher’s teaching quality. Moreover, support vector regression is used to predict the model’s output layer to achieve supervised assessment prediction. To capture the data’s key properties, the model comprises numerous hidden layers and conducts various feature transformations during unsupervised training to minimize the mean square error between the reconstructed output data and the original input data. As a result, the proposed model achieved the highest recognition accuracy of 85.23% and convergence compared to other models. Thus, the method can be employed to evaluate and forecast the quality of university teaching activity successfully.
Automatic comment generation for source code using external information by neural networks for computational thinking

To support the understanding of programs and understanding of procedures, we think need to automatically generate comments from source code. As a method, we learn the source code and comment pair by Encoder–Decoder translation model using LSTM, thereby generating comments of the source code that was the target of learning. In the Encoder–Decoder translation model, the source code of a program and its pair of comments are learned. However, that method does not know the domain of the program being used, so the comments that can be generated automatically may not be appropriate. In the use of learning, such as programs and algorithms for the purpose of computational thinking, it is possible to obtain relate information, such as problem statements and explanations. Therefore, we propose some methods to improve generated comments by using relate external information, such as question sentences. Also, we generate comments for each line and each block.
RECOMMENDER SYSTEMS: AN OVERVIEW

Background. In this article, we look at the key advances in collaborative filtering recommender systems, focusing on the evolution from research focused solely on algorithms to research focused on the broad set of issues surrounding user experience with the recommender. The Internet provides a huge a
Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces

Hate speech and profanity detection suffer from data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation incompatibility of existing corpora.In this study, we identify profane subspaces in word and sentence representations and explore their generalization capability on a variety of similar and distant target tasks in a zero-shot setting.This is done monolingually (German) and cross-lingually to closely-related (English), distantly-related (French) and nonrelated (Arabic) tasks.We observe that, on both similar and distant target tasks and across all languages, the subspace-based representations transfer more effectively than standard BERT representations in the zero-shot setting, with improvements between F1 +10.9 and F1 +42.9 over the baselines across all tested monolingual and cross-lingual scenarios.
An Alignment-Based Implementation of a Holistic Ontology Integration Method

Despite the intense research activity in the last two decades, ontology integration still presents a number of challenging issues. As ontologies are continuously growing in number, complexity and size and are adopted within open distributed systems such as the Semantic Web, integration becomes a central problem and has to be addressed in a context of increasing scale and heterogeneity. In this paper, we describe a holistic alignment-based method for customized ontology integration. The holistic approach proposes additional challenges as multiple ontologies are jointly integrated at once, in contrast to most common approaches that perform an incremental pairwise ontology integration. By applying consolidated techniques for ontology matching, we investigate the impact on the resulting ontology. The proposed method takes multiple ontologies as well as pairwise alignments and returns a refactored/non-refactored integrated ontology that faithfully preserves the original knowledge of the input ontologies and alignments. We have tested the method on large biomedical ontologies from the LargeBio OAEI track. Results show effectiveness, and overall, a decreased integration cost over multiple ontologies. OIAR and AROM are two implementations of the proposed method. OIAR creates a bridge ontology, and AROM creates a fully merged ontology. The implementation includes the option of ontology refactoring.
Discourse Relations and Connectives in Higher Text Structure

The present article investigates possibilities and limits of local (shallow) analysis of discourse coherence with respect to the phenomena of global coherence and higher composition of texts. We study corpora annotated with local discourse relations in Czech and partly in English to try and find clues in the local annotation indicating a higher discourse structure. First, we classify patterns of subsequent or overlapping pairs of local relations, and hierarchies formed by nested local relations. Special attention is then given to relations crossing paragraph boundaries and their semantic types, and to paragraph-initial discourse connectives. In the third part, we examine situations in which annotators incline to marking a large argument (larger than one sentence) of a discourse relation even with a minimality principle annotation rule in place. Our analyses bring (i) new linguistic insights regarding coherence signals in local and higher contexts, e.g. detection and description of hierarchies of local discourse relations up to 5 levels in Czech and English, description of distribution differences in semantic types in cross-paragraph and other settings, identification of Czech connectives only typical for higher structures, or the detection of prevalence of large left-sided arguments in locally annotated data; (ii) as another type of contribution, some new reflections on methodologies of the approaches under scrutiny.
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey

The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long standing problem. The semantic gap problem is reviewed and a survey on recent efforts in bridging the gap is made in this work. Most importantly, we claim that the semantic gap is primarily bridged through supervised learning today. Experiences are drawn from two application domains to illustrate this point: 1) object detection and 2) metric learning for content-based image retrieval (CBIR). To begin with, this paper offers a historical retrospective on supervision, makes a gradual transition to the modern data-driven methodology and introduces commonly used datasets. Then, it summarizes various supervision methods to bridge the semantic gap in the context of object detection and metric learning.
A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.
Regulation of robotics: Analysis of the leading countries’ experience

The paper compares and analyzes the experiences leading states have had in robotics regulation; on the basis of the identifi best practices, proposals are formulated for Russia in the corresponding fi d. The relevance of the research is determined by the rapid growth of the global robotics market over the last decade, as well as the introduction of robots in the widest range of human activities. The leading countries in the international robotics market were selected using the benchmarking method. A comparative analysis of the regulation in the fi d of robotics was conducted through a comparison matrix composed of several criteria. The criteria included the existence of regulations and a national program for the development of robotics, the presence of responsible government bodies or organizations, and sectoral “regulatory sandboxes”. It was discovered that, of the four studied countries (the Republic of Korea, Japan, Germany, and Russia), only one has all the components of the robotics regulation system. The authors formulated certain recommendations aimed at improving the corresponding domestic regulation system. In particular, considering the best practices of foreign countries, the parties concerned are encouraged to adopt a sectoral normative legal act, as well as to establish a specialized state agency.
Word order affects the frequency of adjective use across languages

Author(s): Kachakeche, Zeinab; Futrell, Richard; Scontras, Gregory | Abstract: Recent research has proposed that adjective form (i.e., whether adjectives typically occur before or after the nouns they modify) interacts with considerations from efficient communication to determine the rate at which we use adjectives to resolve reference to objects. According to this efficiency hypothesis, languages with pre-nominal adjectives use modifying adjectives at a higher rate in an effort to aid incremental reference resolution. We test this broad typological prediction in a large-scale corpus analysis of 74 languages, finding that languages that favor pre-nominal adjectives indeed do exhibit higher rates of adjectival modification than languages that favor post-nominal adjectives.
Concrete domains in logics

In this short survey, we present logical formalisms in which reasoning about concrete domains is embedded in formulae at the atomic level. These include temporal logics with concrete domains, description logics with concrete domains as well as variant formalisms. We discuss several proof techniques to solve logical decision problems for such formalisms, including those based on constrained automata or on translation into decidable second-order logics. We also present recent results mainly related to decidability and complexity as well as a selection of open problems.
Time-Efficient Code Completion Model for the R Programming Language

In this paper, we present a deep learning code completion model for the R programming language.We introduce several techniques to utilize language modeling based architecture in the code completion task.With these techniques, the model requires low resources, but still achieves high quality.We also present an evaluation dataset for the R programming language completion task.Our dataset contains multiple autocompletion usage contexts and that provides robust validation results.The dataset is publicly available.
Call for Papers on Machine Learning and Earth System Modeling

Contributions are invited to a new journal special collection on the use of new machine learning methodologies and applications of machine learning to Earth system modeling.
Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication TimeNadiia Chepurko, Kenneth L. Clarkson, Praneeth Kacham, and David P. WoodruffNadiia Chepurko, Kenneth L. Clarkson, Praneeth Kacham, and David P. Woodruffpp.3043 - 3068Chapter DOI:https://doi.org/10.1137/1.9781611977073.118PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In the numerical linear algebra community, it was suggested that to obtain nearly optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns (a basis), regression, or low-rank approximation, a natural way would be to resolve the main open question of Nelson and Nguyen (FOCS, 2013). This question is regarding the logarithmic factors in the sketching dimension of existing oblivious subspace embeddings that achieve constant-factor approximation. We show how to bypass this question using a refined sketching technique, and obtain optimal or nearly optimal bounds for these problems. A key technique we use is an explicit mapping of Indyk based on uncertainty principles and extractors, which after first applying known oblivious subspace embeddings, allows us to quickly spread out the mass of the vector so that sampling is now effective. We thereby avoid a logarithmic factor in the sketching dimension that is standard in bounds proven using the matrix Chernoff inequality. For the fundamental problems of rank computation and finding a basis, our algorithms improve Cheung, Kwok, and Lau (JACM, 2013), and are optimal to within a constant factor and a poly(log log(n))-factor, respectively. Further, for constant-factor regression and low-rank approximation we give the first optimal algorithms, for the current matrix multiplication exponent. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Privacy-Aware Data Trading

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.
Online distance education system in remote region in the Russian and Mongolian universities during the COVID-19 pandemic

The objective of the study is to compare the role of faculty between two universities in restriction regime conditions, to identify problems and future perspectives for the transformation of information technologies in universities. The study was conducted among 145 lecturers from Russia and 102 lecturers from Mongolia. The questionnaire consists of 20 questions related to the research topic. The anonymous study was conducted in July 2020 after the second academic semester. The analysis of the resulting survey showed 100% adaptability to online learning of the lecturers in the National University of the Medical Science in Darkhan and 75% adaptability to online learning of the lecturers in Trans-Baikal State University when they moved to the online learning approach. The effectiveness and quality of the transition to the online learning approach principally depend on the professional qualities of lecturers. The lecturers of the Mongolian university had an adequately high level of application of modern information and communication technology in distance learning in comparison with the lectures in Trans-Baikal State University. The implementation of the educational online process by lecturers should be focused on obtaining high-quality higher education, two university students. Survey findings point to the need for universities to continue to improve digital transformation, which is currently one of the most effective ways to engage students in the educational process.
IS BITCOIN IMMUNE TO THE COVID-19 PANDEMIC?

This study examines how Bitcoin’s trading characteristics react to the COVID-19 pandemic, using detailed futures trading data from the Chicago Mercantile Exchange. The results show that volume-weighted Bitcoin futures return responds positively to the spikes of public interest. Meanwhile, the surges of pandemic information do not harm market quality. Volume, bid-ask spread, and trading frequency remain stable, indicating that the positive price reaction is not a result of a few small uninformed trades. Bitcoin's conditional beta on the S&amp;P 500 index drops to near zero, while the conditional beta on gold more than doubles. These results indicate that traders have been using Bitcoin as a safe-haven asset after the pandemic outbreak.
Hashtag Analysis of Indonesian COVID-19 Tweets Using Social Network Analysis

Social media has become more critical for people to communicate about the pandemic of COVID-19. In social media, hashtags are social annotations which often used to denote message content. It serves as an intuitive and flexible tool for making huge collections of posts searchable on Twitter. Through practices of hashtagging, user representations of a given post also become connected. This study aimed to analyze the hashtag of Indonesian COVID-19 Tweets using Social Network Analysis (SNA). We used SNA techniques to visualize network models and measure some centrality to find the most influential hashtag in the network. We collected and analyzed 500.000 public tweets from Twitter based on COVID-19 keywords. Based on the centrality measurement result, the hashtag #corona is a hashtag with the most connection with other hashtags. The hashtag #COVID19 is the hashtag that is most closely related to all other hashtags. The hashtag #corona is the hashtag that most acts as a bridge that can control the flow of information related to COVID-19. The hashtag #coronavirus is the most important of hashtags based on their link. Our study also found that the hashtag #covid19 and #wabah have a substantial relationship with religious-related hashtags based on network visualization.
AthPPA: A Data Visualization Tool for Identifying Political Popularity over Twitter

Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in-real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there.
A Streaming End-to-End Framework For Spoken Language Understanding

End-to-end spoken language understanding (SLU) recently attracted increasing interest. Compared to the conventional tandem-based approach that combines speech recognition and language understanding as separate modules, the new approach extracts users' intentions directly from the speech signals, resulting in joint optimization and low latency. Such an approach, however, is typically designed to process one intent at a time, which leads users to have to take multiple rounds to fulfill their requirements while interacting with a dialogue system. In this paper, we propose a streaming end-to-end framework that can process multiple intentions in an online and incremental way. The backbone of our framework is a unidirectional RNN trained with the connectionist temporal classification (CTC) criterion. By this design, an intention can be identified when sufficient evidence has been accumulated, and multiple intentions will be identified sequentially. We evaluate our solution on the Fluent Speech Commands (FSC) dataset and the detection accuracy is about 97 % on all multi-intent settings. This result is comparable to the performance of the state-of-the-art non-streaming models, but is achieved in an online and incremental way. We also employ our model to an keyword spotting task using the Google Speech Commands dataset, and the results are also highly promising.
Virtual Mini-Lecture in Distance Learning Space

The COVID-19 pandemic has fundamentally affected all the spheres of human life, including education. Universities worldwide have established distance learning, which demanded combining and improving both traditional and innovative education forms. The article offers a comprehensive analysis of pre-recorded mini-lectures in the educational space of distance learningfor different scientific-educational Internet projects (Coursera, PostNauka, Udacity, edX, LinguaLeo, EdEra, etc.). Mini-lectures are complete pieces of speech limited in time and accessible to multiple reproductions; that is why they are quite a representative material for the research of the generation of cognitive processes and perception of a verbal educational message in asynchronous online learning. The significant results of the study include: the definition of invariable characteristics of virtual mini-lectures (distance, virtuality, mediation, situational incorrigibility, multimodality), defining cognitive strategies of the speaker and the listener in an asynchronous lecture interaction, finding the points of contact (motivation and interest in transferring and receiving information) between them. The article also describes the linguistic features of virtual mini-lecture. These conclusions can find practical application in the development of methodology for the distance teaching of linguistic disciplines.