Reviewerly/exHarmony huggingface dataset:
University Youth and Science: from the Experience of the University Associated with the IPA of EurAsEC

The University associated with the IPA of EurAsEC is one of the non–state universities in Russia, which has chosen Eurasian integration as the main direction of its activity since its formation. It is attended by young peo-ple from different Eurasian states. Taking into account the evolution of the regional integration process, the University develops professional training of innovative personnel for the countries of Greater Eurasia, including methods of ethnopedagogy in its content and relying on traditional values. One of the most important aspects of educational activity is the inclusion of scientific achievements in the educational process and the involve-ment of students in the implementation of research based on domestic experience. To this end, new forms of organizing the research activities of young scientists from different countries have been undertaken, which gradually effectively supplemented the system of organizing the scientific search of students.
Inverse Identification of a 3D Anisotropic Yield Function Through an Information-Rich Tensile Test and Multi-sDIC

In the case of thick high strength steels, 3D anisotropic yield criteria are required to accurately capture the plastic material response. Material testing 2.0 aims at extracting material behaviour from an information-rich mechanical experiment with aid of inverse methods and full-field measuring techniques. In this context, inverse identification of 3D anisotropy through a single experiment is particularly challenging because of the large amount of anisotropy parameters. The difficulty lies in the specimen design that ideally ensures a simultaneous activation of all sought anisotropy parameters with sufficient sensitivity towards the observed strain field. In this contribution, we scrutinize the effectiveness of a specimen designed by shape optimization to inversely identify a 3D anisotropic yield function. This is done via a so-called Digital Virtual Twin (DVT) mimicking the actual measurement including the Digital Image Correlation (DIC). Given that the synthetic data is generated with a known material model used by the FE model, the identification accuracy of the Finite Element Model Updating (FEMU) approach can be objectively assessed. The novelty here is that the synthetic data consists of the data captured by two stereo-DIC (sDIC) systems. Moreover, the FEMU approach accounts for the multi-sDIC data sets. The DVT-results show that the adopted specimen design enables to simultaneously identify the sought anisotropy parameters. The proposed methodology is experimentally validated on S700MC steel with a thickness of 12 mm.
Тhe positive implications of the decentralization process on primary education management

Democratization has emerged as a need for the good of citizens, to improve the quality of life, the process of accession to communities (democratization of society, creating a democratic, accountable, transparent and accountable government) and to improve and maintain stable interethnic relations in the country. As in other segments, decentralization in education refers to the transfer of responsibilities from national to local level, thus placing primary and secondary education under the jurisdiction of municipalities. The decentralization process is changing the way schools are managed and managed where schools tend to become open learning centers, involving more entities, including parents and students, and local government officials, increasing cooperation with enterprises and NGOs such as and utilizing local resources in order to enrich the experience and raise the level of skills acquired. The research part is aimed at determining the effects of primary education management before and after the decentralization process. The research was conducted on a sample of respondents from primary school teachers, principals, ie deputy principals and professional associates from the pedagogical-psychological service in schools. A research questionnaire technique was used to conduct the research. The poll is anonymous and the questions are clearly worded. The obtained results from the research are processed quantitatively, and certain areas will be interpreted qualitatively.
LBA-PAKE: Lattice-Based Anonymous Password Based Authentication Key Exchange Scheme for VANET

The Discrete logarithmic problem (DLP) or prime factorization problems are employed in key exchange protocol’s security. But now Shor has given an algorithm that can resolve these problems on quantum computers in polynomial time. Classical cryptography assures the security of the numerous authentication key exchange (AKE) protocols that are already present in VANET. But in the quantum computing era, these protocols were not secured by quantum attacks. In this paper, we proposed a protocol that is an anonymous password based authenticated key exchange for VANETs which is postquantum era secured. Further, our proposed protocol supports perfect forward secrecy (PFS), key reuse, and anonymity features. Finally, our proposed protocol’s security analysis shows that it is secure and efficient.
Detection of Phishing Threats Using Neural Networks

Today, the Internet is an effective channel for social interaction worldwide, but it also opens up great opportunities for cyberattacks. Recently, the number of botnets and phishing cyberattacks has increased dramatically. Phishing is a cyber attack carried out using a fake website designed to steal sensitive information. Detecting such attacks using machine learning algorithms and neural systems is an efficient and fast method. This paper discusses the types of Internet threats and neural network algorithms used to counter phishing attacks. The main goal of this paper is to build neural network models capable of effectively detecting phishing attacks. The methods of research are data analysis, data preprocesses and neural networks. In the first step, the Kaggle dataset containing data from 5000 legitimate and 5000 malicious web pages was gathered. The features of the dataset were analyzed and normalized. Then the Deep neural network, Convolutional neural network and Long short-term memory network were applied for threat classification. The Deep neural network and Long short-term memory network showed the best results of accuracy up to 0.96 and 0.99, while the Convolutional neural network reached only 0.75. Generally, the Deep neural network and Long short-term memory achieved the best results among the neural network methods.
Analysis of The Multilayer Perceptron Algorithm on Twitter User’s Sentiment Towards The COVID-19 Vaccine

The World Health Organization (WHO) declared COVID-19 a global pandemic due to its rapid spread and infection of people worldwide. The emergence of COVID-19 vaccines has garnered both support and rejection from the public. Some people support the vaccines, while others remain cautious, even though the government provides them for free. The procurement of coronavirus vaccines has generated diverse opinions in society. COVID-19 vaccines have become a trending topic on social media, particularly on Twitter. This research aims to explore public opinions on the COVID-19 vaccine. The methods used in this study include data collection, text preprocessing, TF-IDF, multilayer perceptron algorithm, and testing with confusion matrices. Out of a total of 228,208 positive, negative, and neutral opinions from Twitter users about the COVID-19 vaccine, with a training-to-testing ratio of 90% to 100%, the model will learn more by using a large amount of training data. The performance results of this research obtained the highest accuracy of 81.2%, precision of 83.8%, and recall of 71.2%. The results of sentiment analysis can be seen in the public opinions on the COVID-19 vaccine, which are divided into three categories: 35% positive opinions, 16.3% negative opinions, and 48.7% neutral opinions. The word cloud results show that positive opinions revolve around three topics: availability, cost, and dosage. Negative opinions from Twitter users about the COVID-19 vaccine focus on two main issues: vaccine side effects and deaths. Neutral opinions cover three topics, including dosage, availability, age, and expiration date
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning

Cross-domain Sequential Recommendation (CSR) which leverages user sequence data from multiple domains has received extensive attention in recent years. However, the existing CSR methods require sharing origin user data across domains, which violates the General Data Protection Regulation (GDPR). Thus, it is necessary to combine federated learning (FL) and CSR to fully utilize knowledge from different domains while preserving data privacy. Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL. In this paper, we propose FedDCSR, a novel federated cross-domain sequential recommendation framework via disentangled representation learning. Specifically, to address the sequence feature heterogeneity across domains, we introduce an approach called inter-intra domain sequence representation disentanglement (SRD) to disentangle the user sequence features into domain-shared and domain-exclusive features. In addition, we design an intra domain contrastive infomax (CIM) strategy to learn richer domain-exclusive features of users by performing data augmentation on user sequences. Extensive experiments on three real-world scenarios demonstrate that FedDCSR achieves significant improvements over existing baselines.
The Teaching Strategy in Reading Comprehension in Grade 2 Students of MtsN Serdang Bedagai

This research was aimed to figure out the strategies used by English teachers in teaching reading and how they applied the strategies. It was designed as descriptive qualitative research. Participants of the research were two English teachers who taugh the 2 grade the students. Each English teacher had eight meetings observed by the researcher. The research instruments aere strategies checklist and interview. They were used to figure out the strategies used and how the English teachers applied the strategies in teaching reading comprehension. The result of the data were analyzed and broken down into three stages or Data Reduction, Data Display, and Drawing Conclusion or Interpretation. The previous studies showed that the teachers’ strategies were very effective in teaching reading comprehension. On the other hand, the use of too many strategies would affect students’ performances in learning reading comprehension. This reason was contradicted with the conducted the researcher in Junior High School Number 9 Bengkulu. This research showed that the teachers in applying strategies only use two or three strategies. Combination of some strategies was more appropriate comparemany strategies used by the teachers. The students would be more focused if the teacher used fewer strategies but well applied in teaching reading comprehension.
An Analysis of Students’ Speaking Skill through British Parliamentary Debate System

The purpose of this research was to investigate the students’ speaking skill through British Parliamentary (BP) debate System. This observation was taken 18 Days which started from Thursday, 13th February to 12 March 2020. The Qualitative Research Method was used in this research. The subjects of this research chosen randomly were 8 student as the representative of students in the fourth semester of English education UMN Al Washliyah Medan Class A, academic year 2019-2020. The population from this research were the whole students in the 4th semester English Education Department Class A.Technique of collecting data used was the observation in which the students were observed during debate class by using observation sheet designed with theory of Finocchiaro in Armasita (2017) to know the ability that have been achieved by the students and all the data found from 8 subjects of research that have been analyzed by the researcher there were 4 indicators/elements, namely pronunciation, vocabulary, fluency and comprehension. After the research conducted, it was found that there were 3 students with the higher speaking skill, 4 students with the moderate speaking skill, and only one student categories as the unsatisfied speaking skill. Shortly, this finding of the research was appropriate with the assumption in this research that is debate is effective to increase the students’ speaking skill.
The Effectiveness of Using Bandicam in Students’ Speaking English During Midst Covid 19 Pandemic

The study aimed to explore the effectiveness of Bandicam in students’ speaking English at SMA Negeri 1 Merbau from the perspective of the students. A five point Linkert questionnaire was used and a dscriptive analytical approach was adopted. Through the pusposive sampling method, the researcher selected a sample consisting from 36 students. Those students were selected from XI IBB. The questionnaire forms were distributed by hand to all of the selected students. All of forms were filled and retrieved. However, 35 forms are considered valid for analysis. SPSS program was used. The researcher found the Bandicam palyed an effective media in students’ speaking English from the students’ perspective. It was found that Bandicam was capable of assessing students’ amount of speaking effectively. It was found that Bandicam enabled instructors to save their effort. It was found that Bandicam hindered from commiting acts of cheating.
The Effect of English Audio Visual Towards Students’ English Listening Skill

The objective of the research was to find out whether there is significant effect of using media audio visual on students’ competency in listening skill. This research was conducted in MAS AL-MANAR Medan students of tenth grade. This research used quantitative research with experimental design with two classes. Control class consisted of 20 students and experimental class consisted 21 students. This research used media audio visual as learning method. The technique of collecting data of this research were pre-test, treatment, and post-test. With each test comes 10 questions of multiple choice. The results obtained in this research proved that there was a different score on students’ in learning listening by using media audio visual and used media audio. Students’ achieved good grades but in the control class it was lower than the experimental class. It could be show as follows that the t-test value was 14.735 with df= n-2 = 39, tobserved &gt;ttable, tobserved was higher than ttable with 2.0227. It can be concluded that Media Audio Visual can be affected on students’ listening skill.
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees

Tabular data is hard to acquire and is subject to missing values. This paper introduces a novel approach for generating and imputing mixed-type (continuous and categorical) tabular data utilizing score-based diffusion and conditional flow matching. In contrast to prior methods that rely on neural networks to learn the score function or the vector field, we adopt XGBoost, a widely used Gradient-Boosted Tree (GBT) technique. To test our method, we build one of the most extensive benchmarks for tabular data generation and imputation, containing 27 diverse datasets and 9 metrics. Through empirical evaluation across the benchmark, we demonstrate that our approach outperforms deep-learning generation methods in data generation tasks and remains competitive in data imputation. Notably, it can be trained in parallel using CPUs without requiring a GPU. Our Python and R code is available at https://github.com/SamsungSAILMontreal/ForestDiffusion.
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

Communication efficiency is a major challenge in federated learning (FL). In client-server schemes, the server constitutes a bottleneck, and while decentralized setups spread communications, they do not necessarily reduce them due to slower convergence. We propose Multi-Token Coordinate Descent (MTCD), a communication-efficient algorithm for semi-decentralized vertical federated learning, exploiting both client-server and client-client communications when each client holds a small subset of features. Our multi-token method can be seen as a parallel Markov chain (block) coordinate descent algorithm and it subsumes the client-server and decentralized setups as special cases. We obtain a convergence rate of $\mathcal{O}(1/T)$ for nonconvex objectives when tokens roam over disjoint subsets of clients and for convex objectives when they roam over possibly overlapping subsets. Numerical results show that MTCD improves the state-of-the-art communication efficiency and allows for a tunable amount of parallel communications.
A General Framework for the Practical Disintegration of PAC-Bayesian Bounds

PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers. However, they require a loose and costly derandomization step when applied to some families of deterministic models such as neural networks. As an alternative to this step, we introduce new PAC-Bayesian generalization bounds that have the originality to provide disintegrated bounds, i.e., they give guarantees over one single hypothesis instead of the usual averaged analysis. Our bounds are easily optimizable and can be used to design learning algorithms. We illustrate this behavior on neural networks, and we show a significant practical improvement over the state-of-the-art framework.
Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults

Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data. However, this progress doesn't readily extend to ASR for children due to the limited availability of suitable child-specific databases and the distinct characteristics of children's speech. A recent study investigated leveraging the My Science Tutor (MyST) children's speech corpus to enhance Whisper's performance in recognizing children's speech. They were able to demonstrate some improvement on a limited testset. This paper builds on these findings by enhancing the utility of the MyST dataset through more efficient data preprocessing. We reduce the Word Error Rate (WER) on the MyST testset 13.93% to 9.11% with Whisper-Small and from 13.23% to 8.61% with Whisper-Medium and show that this improvement can be generalized to unseen datasets. We also highlight important challenges towards improving children's ASR performance. The results showcase the viable and efficient integration of Whisper for effective children's speech recognition.
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images

Breast cancer is one of the leading causes of death for women worldwide. Early screening is essential for early identification, but the chance of survival declines as the cancer progresses into advanced stages. For this study, the most recent BRACS dataset of histological (H\&E) stained images was used to classify breast cancer tumours, which contains both the whole-slide images (WSI) and region-of-interest (ROI) images, however, for our study we have considered ROI images. We have experimented using different pre-trained deep learning models, such as Xception, EfficientNet, ResNet50, and InceptionResNet, pre-trained on the ImageNet weights. We pre-processed the BRACS ROI along with image augmentation, upsampling, and dataset split strategies. For the default dataset split, the best results were obtained by ResNet50 achieving 66% f1-score. For the custom dataset split, the best results were obtained by performing upsampling and image augmentation which results in 96.2% f1-score. Our second approach also reduced the number of false positive and false negative classifications to less than 3% for each class. We believe that our study significantly impacts the early diagnosis and identification of breast cancer tumors and their subtypes, especially atypical and malignant tumors, thus improving patient outcomes and reducing patient mortality rates. Overall, this study has primarily focused on identifying seven (7) breast cancer tumor subtypes, and we believe that the experimental models can be fine-tuned further to generalize over previous breast cancer histology datasets as well.
Projected Task-Specific Layers for Multi-Task Reinforcement Learning

Multi-task reinforcement learning could enable robots to scale across a wide variety of manipulation tasks in homes and workplaces. However, generalizing from one task to another and mitigating negative task interference still remains a challenge. Addressing this challenge by successfully sharing information across tasks will depend on how well the structure underlying the tasks is captured. In this work, we introduce our new architecture, Projected Task-Specific Layers (PTSL), that leverages a common policy with dense task-specific corrections through task-specific layers to better express shared and variable task information. We then show that our model outperforms the state of the art on the MT10 and MT50 benchmarks of Meta-World consisting of 10 and 50 goal-conditioned tasks for a Sawyer arm.
Heteroscedastic sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm

Sparse linear regression methods for high-dimensional data commonly assume that residuals have constant variance, which can be violated in practice. For example, Aphasia Quotient (AQ) is a critical measure of language impairment and informs treatment decisions, but it is challenging to measure in stroke patients. It is of interest to use high-resolution T2 neuroimages of brain damage to predict AQ. However, sparse regression models show marked evidence of heteroscedastic error even after transformations are applied. This violation of the homoscedasticity assumption can lead to bias in estimated coefficients, prediction intervals (PI) with improper length, and increased type I errors. Bayesian heteroscedastic linear regression models relax the homoscedastic error assumption but can enforce restrictive prior assumptions on parameters, and many are computationally infeasible in the high-dimensional setting. This paper proposes estimating high-dimensional heteroscedastic linear regression models using a heteroscedastic partitioned empirical Bayes Expectation Conditional Maximization (H-PROBE) algorithm. H-PROBE is a computationally efficient maximum a posteriori estimation approach that requires minimal prior assumptions and can incorporate covariates hypothesized to impact heterogeneity. We apply this method by using high-dimensional neuroimages to predict and provide PIs for AQ that accurately quantify predictive uncertainty. Our analysis demonstrates that H-PROBE can provide narrower PI widths than standard methods without sacrificing coverage. Narrower PIs are clinically important for determining the risk of moderate to severe aphasia. Additionally, through extensive simulation studies, we exhibit that H-PROBE results in superior prediction, variable selection, and predictive inference compared to alternative methods.