Reviewerly/exHarmony huggingface dataset:
BERTimbau in Action: An Investigation of its Abilities in Sentiment Analysis, Aspect Extraction, Hate Speech Detection, and Irony Detection.

Social Media has revolutionized how individuals, groups, and communities interact. This immense quantity of unstructured data holds valuable information expressed in informal language. However, automatically extracting this information using Natural Language Processing requires adaptations of traditional methods or the development of new strategies capable of extracting information tackling web-prone language. BERT, a Deep Learning methodology proposed by Google in 2018, brought transfer learning to Natural Language Processing. In this work, we used a BERT model for the Portuguese language called BERTimbau to create models for Sentiment Analysis, Aspect Extraction, Hate Speech Detection, and Irony Detection. We experimented with the two BERTimbau models, base and large. Finally, we compared the results obtained in each task. Experiments with BERTimbau based models obtained improved results, F-Measure of 0.88 and 0.89 in Sentiment Analysis and Hate Speech Detection tasks, respectively, compared to classical Machine Learning approaches.
Covering radius of RM(4,8)

We propose an effective version of the lift by derivation, an invariant that allows us to provide the classification of B(5,6,8)=RM(6, 8)/RM(4,8). The main consequence is to establish that the covering radius of the Reed-Muller RM(4,8) is equal to 26.
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion

Diffusion-based generative models' impressive ability to create convincing images has captured global attention. However, their complex internal structures and operations often make them difficult for non-experts to understand. We present Diffusion Explainer, the first interactive visualization tool that explains how Stable Diffusion transforms text prompts into images. Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations, enabling users to fluidly transition between multiple levels of abstraction through animations and interactive elements. By comparing the evolutions of image representations guided by two related text prompts over refinement timesteps, users can discover the impact of prompts on image generation. Diffusion Explainer runs locally in users' web browsers without the need for installation or specialized hardware, broadening the public's education access to modern AI techniques. Our open-sourced tool is available at: https://poloclub.github.io/diffusion-explainer/. A video demo is available at https://youtu.be/Zg4gxdIWDds.
Asteriscul. Un semn cu multiple valori

"The asterisk is one of the oldest graphic signs and still plays an important role in both printed and electronic texts. The sign * appears in a wide variety of domains (literary texts, scientific works, legal and administrative documents, advertising messages), and the roles differ depending on the type of text. Some roles played by the asterisk remain constant regardless of the domain: for example, marking footnotes, delimiting chapters or anonymizing proper names. Other roles are specific to a certain field: marking unattested and reconstituted forms or ungrammatical constructions in linguistics or marking bold text in communication on electronic devices that do not allow bold letters."
Subject Properties of the Future Teacher in the System of Professional Training

The relevance of the study is determined by the change in views on the professional training of specialists in the pedagogical sphere in accordance with the humanisation and subjectivisation of the socio-cultural space and the need to improve the educational process. The purpose of the article is to analyse modern scientific developments on the problem of developing the subjectivity of future teachers of education to establish their subject characteristics. The research was conducted based on general scientific theoretical research methods, in particular system analysis and generalization. The article analyses the problem of developing the subjectivity of future teachers in scientific and pedagogical literature and practice. The concept of “development of future specialists in the pedagogical field” is defined. The main directions of development of future teachers, including personal, social and professional, are considered. Psychological conditions for the formation of professional development of the individual are highlighted. The main characteristics of the subject of activity are highlighted, based on which the subject characteristics of the future teacher are determined, which, for its part, includes: the orientation of the individual, professional and psychological competence. It is established that development management of subjectivity of a future teacher as a subject of professional activity can be carried out in conditions that meet the requirements of creative professional activity. The practical value of scientific work is to determine the key subject characteristics of future employees of the educational sphere, which in the future will contribute to the development of a management system for improving the subject aspect of each individual teacher and practical updating of the educational process in particular
HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction

Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction. Existing methods either tackle these two tasks separately or unify them with word-by-word interactions. In this paper, we propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the high-order interactions, i.e., the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. For this purpose, we first devise a W-shape DNN (WNet) to capture coarse-level high-order connections. Then, we build a heuristic high-order graph and further calibrate the representations with a graph neural network (GNN). Experiments on three benchmarks (ACE04, ACE05, SciERC) show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.1~1.8 F1 points over the prior best unified model.
PromptRank: Unsupervised Keyphrase Extraction Using Prompt

The keyphrase extraction task refers to the automatic selection of phrases from a given document to summarize its core content. State-of-the-art (SOTA) performance has recently been achieved by embedding-based algorithms, which rank candidates according to how similar their embeddings are to document embeddings. However, such solutions either struggle with the document and candidate length discrepancies or fail to fully utilize the pre-trained language model (PLM) without further fine-tuning. To this end, in this paper, we propose a simple yet effective unsupervised approach, PromptRank, based on the PLM with an encoder-decoder architecture. Specifically, PromptRank feeds the document into the encoder and calculates the probability of generating the candidate with a designed prompt by the decoder. We extensively evaluate the proposed PromptRank on six widely used benchmarks. PromptRank outperforms the SOTA approach MDERank, improving the F1 score relatively by 34.18%, 24.87%, and 17.57% for 5, 10, and 15 returned results, respectively. This demonstrates the great potential of using prompt for unsupervised keyphrase extraction. We release our code at https://github.com/HLT-NLP/PromptRank.
Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation

The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.
Testing exchangeability in the batch mode with e-values and Markov alternatives

The topic of this paper is testing exchangeability using e-values in the batch mode, with the Markov model as alternative. The null hypothesis of exchangeability is formalized as a Kolmogorov-type compression model, and the Bayes mixture of the Markov model w.r. to the uniform prior is taken as simple alternative hypothesis. Using e-values instead of p-values leads to a computationally efficient testing procedure. In the appendixes I explain connections with the algorithmic theory of randomness and with the traditional theory of testing statistical hypotheses. In the standard statistical terminology, this paper proposes a new permutation test. This test can also be interpreted as a poor man's version of Kolmogorov's deficiency of randomness.
High-Fidelity Transport of Trapped-Ion Qubits in a Multi-Layer Array

A variety of physical platforms are investigated for quantum control of many particles, and techniques are extended to access multiple dimensions. Here, we present our experimental study of shuttling single Mg$^+$ ions within a scalable trap-array architecture that contains up to thirteen trapping sites in a three-dimensional arrangement. We shuttle ions from a dedicated loading hub to multiple sites with a success rate of larger than $0.99999$. In a prototype application, we demonstrate the preservation of the coherence of superposition states of a hyperfine qubit during inter-site shuttling. Our findings highlight the potential of these techniques for use in future large-scale architectures.
Short Term Power Load Forecasting Based on Image Matrix

Aiming at the problem of short-term power load forecasting, this paper proposes a load forecasting method based on image matrix. This method converts the time series of power load into a visual graph(VG), then extracts statistical features from the visual graph, captures the fine-grained features of the time series, and constructs the image matrix of the sequence. Finally, it inputs the load forecasting model based on convolutional neural network. Compared with the traditional load features based on time series, the image matrix has more fine-grained load features. The experimental results show that the short-term load forecasting accuracy of the proposed is better than the time series.
Abnormal data detection method of power system based on attention mechanism

Abnormal data detection is a key link to ensure data quality in power systems. This paper proposes an attention mechanism-based abnormal data detection method for power system. First, the timing characteristics of scheduling measurement data in different statuses of the system are analysed. On this basis, a system status identification method based on stacking strategy is proposed. Furthermore, for the scheduling measurement data in different statuses, a long short-term memory network is used for learning and training, and an attention mechanism based on the time series feature space is introduced to realize the time series interpretability of the model, thereby improving the detection accuracy of abnormal data. Finally, the proposed method is verified by using the simulation data and the measured data respectively. The results show that the proposed method can effectively detect abnormal data in the control system and lay a foundation for subsequent business analysis.
Distribution network disaster assessment

This paper studies the disaster early warning technology of distribution network. Mainly from the risk of disaster causing factors, the sensitivity of disaster pregnant environment and the disaster resistance ability of disaster bearing body, select these three factors and relevant evaluation indexes respectively, use the improved analytic hierarchy process and coefficient of variation method to obtain the subjective weight and objective weight of each index respectively, then use the combined weight method to obtain the comprehensive weight, and finally use the fuzzy comprehensive evaluation method to obtain the corresponding evaluation results of distribution network. Finally, an example is given to verify the accuracy and reliability of the evaluation model.
Education in pandemic times: A student vision of the contrast between presential and online education

With the goal of know and evaluate the face-to-face education previous to the pandemic and contrasting it with the education accomplished during the COVID 19 pandemic, this research was carried out on undergraduate students. A survey was applied, previously validated (Ortiz-Sánchez et al., 2021), the results allow to know aspects related to ideas that students have expressed during the exercise of online classes and in pre-pandemic. The research was carried out with students of different levels and areas of academic preparation. The results show that in higher education there is a great sense of adaptation to both forms of learning, with a slight tendency to be more interested in face-to-face education, with no significant differences between the opinions shown by the results between online education and face-to-face education. The known of these data can provide the establishment of actions and direction to appropriate methodologies for the future development of the emerging mixed education.
Formative assessment for virtual education in professional studies

The Bachelor of Graphic Design (BGD) of a University in the State of Mexico, presented difficulties in the system of evaluation, notwithstanding applying evaluation only through qualifications, without considering the formative evaluation system. Therefore, this study is focused on incorporate formative assessment into the subjects of the BGD curriculum through the model of Dick y Carey (2015); the objective generated instructional alternatives to strengthen the teaching-learning process of the subjects in virtual modality. For that reason, each of the elements of the model is presented, which is made up of phases, evaluators, dates, objectives, and instruments, as well as the conclusions of the research.
Automatic identification of sentiment in unstructured text

The constant increase of information in digital format forces us to have new tools that allow us to download, organize and analyze the information available on the web. One of the analyses performed on unstructured information is polarity identification. In this paper we present a method to carry out polarity identification in unstructured texts. Specifically, texts downloaded from the social network Twitter are used. The current popularity of social networks, has caused a great prominence among different users for the generation of information day by day. Twitter presents us with a great challenge in the automatic processing of natural language, mainly when the number of opinions is very large and automatic processing is required. In our case, in the determination of the polarity contained in a tweet. In this paper we present results obtained using different machine learning methods widely known in the state of the art, such as: Support Vector Machine, Naive Bayes, Logistic Regression, Nearest Neighbors and Random Forest, which are used in two implemented classification scenarios: cross-validation and training and test sets. Two data sets are used for the evaluation of the implemented methodology. The best results are obtained with Support Vector Machine for both datasets, the obtained accuracy values higher than 83 % allow to see the viability of the implemented methodology.
A systematic modelling of recommender system using derivative imitation of SVD and regression

Collaborative filtering has been extensively used in recommender systems, and its adoption is aided by the abundance of actual and trustworthy user data available in the era of big-data. However, as consumers' knowledge of information security grows, these data either disappear or deteriorate in quality. Matrix factorization approach - SVD (Singular Value Decomposition) is often utilised in Collaborative Filtering (CF) systems. It adds bias information about users and products and is accomplished with the use of algebraic feature extraction. Due to the inclusion of implicit feedback information, the SVDpp, derivative model of SVD obtains a higher prediction accuracy. But it also struggles to produce accurate predictions in case of highly sparse datasets. In this paper, a hybrid model blending the functionality of SVDpp and regression technique is proposed. The performance of proposed hybrid model is compared with other baseline models such as: KNNBasic, KNN Means, KNN ZScore, SVD, Non-negative Matrix Factorization (NMF), SlopeOne and Co-Clustering technique. The performance testing is done on MovieLens-100K dataset. The proposed model outperformed the baseline approaches with relatively better significance in terms of prediction accuracy.
Cam teach and prosper? EFL teachers’ attitudes, well-being, and coping strategies in an online setting

Abstract The global Covid-19 pandemic led to the turn toward online learning and the need to adapt to new ways of English foreign language (EFL) teaching. This mixed-methods research aimed to examine teacher attitudes, well-being, and coping strategies related to online EFL teaching. The results indicated that Croatian EFL teachers had a positive attitude toward online teaching and acknowledged the importance of being digitally competent. EFL teachers were unsure about their ability to deal with online teaching and indicated their need to improve their IT skills. They revealed more negative than positive responses regarding their well-being and showed that they tried to actively cope during the pandemic. Results also showed better coping strategies among female teachers.
A Novel Approach for Service Selection and Ranking in Federated Cloud

In Federated Cloud environment, Cloud service ranking and selection is a very tedious work because of complexion involved Service Level Agreement (SLA) and Service specifications. Quality of Service (QoS) expected from Cloud users may be conflicting based on their applications. Cloud Brokers needs to be deal with Cloud users and Federated cloud like a heterogeneous interface. A Cloud Brokers also fulfils many suitable services from available resources. Cloud Broker also choose the federated cloud services based on their rank for available service. Noncommercial and conflicting demands of users evolves Cloud Service Selection a very competitive multi-criteria based service ranking and selection problem. Cloud User requests from different federated clouds based on some preference order of QoS parameters. This preference order converts into individual QoS parameters by assigning suitable weight. The weighted demands are estimated the available QoS values for each cloud service. In the next step, modified VIKOR method is applied for finding the rank of the services. This rank of service is based on preference of QoS parameters. The service with highest rank is selected and provided to the user. We used CloudSim for the testing of this method.
Learn to Unlearn: A Survey on Machine Unlearning

Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on removing the influence of specific data samples from a trained ML model. Such efficient removal would enable ML to comply with the "right to be forgotten" in many legislation, and could also address performance bottlenecks from low-quality or poisonous samples. In that context, machine unlearning methods have been proposed to erase the contributions of designated data samples on models, as an alternative to the often impracticable approach of retraining models from scratch. This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We further highlight emerging challenges and prospective research directions (e.g. resilience and fairness concerns). We aim for this paper to provide valuable resources for integrating privacy, equity, andresilience into ML systems and help them "learn to unlearn".
Alternative mechanisms of managing the education’s development in the social investment model of Russia’s economic growth: standardization versus deregulation

Regression analysis is used to determine the contribution of the factors of managing the development of higher education – share of university branches – to increase the population’s quality of life. To determine the optimal approach to managing education’s development in the social investment model of Russia’s economic growth with alternative mechanisms of standardization and deregulation. The results of the research show that absolute standardization, as well as absolute deregulation of higher education, does not allow achieving significant results in the sphere of increase in quality of life in Russia. The determined and substantiated optimal scenario of managing the development of higher education in Russia to increase the quality of life envisages a foundation on a flexible approach, which envisages the combination of standardization and deregulation. It is recommended to entirely refuse branches in favor of main universities, refuse government financing in favor of fee-based education and refuse diversification of education forms in favor of the intramural form of study. This allows raising the quality of life by 13.26% (up to 70.87 points). For practical implementation of this approach, it is offered to allow each region to form their own programs of development of higher education with the foundation of the main federal principles.
Linearizability Analysis of the Contention-Friendly Binary Search Tree

We present a formal framework for proving the correctness of set implementations backed by binary-search-tree (BST) and linked lists, which are often difficult to prove correct using automation. This is because many concurrent set implementations admit non-local linearization points for their `contains' procedure. We demonstrate this framework by applying it to the Contention-Friendly Binary-Search Tree algorithm of Crain et al. We took care to structure our framework in a way that can be easily translated into input for model-checking tools such as TLA+, with the aim of using a computer to verify bounded versions of claims that we later proved manually. Although this approach does not provide complete proof (i.e., does not constitute full verification), it allows checking the reasonableness of the claims before spending effort constructing a complete proof. This is similar to the test-driven development methodology, that has proven very beneficial in the software engineering community. We used this approach and validated many of the invariants and properties of the Contention-Friendly algorithm using TLA+. It proved beneficial, as it helped us avoid spending time trying to prove incorrect claims. In one example, TLA+ flagged a fundamental error in one of our core definitions. We corrected the definition (and the dependant proofs), based on the problematic scenario TLA+ provided as a counter-example. Finally, we provide a complete, manual, proof of the correctness of the Contention-Friendly algorithm, based on the definitions and proofs of our two-tiered framework.