Reviewerly/exHarmony huggingface dataset:
Pedagogical role of assessment at the current stage of stimulating the educational process

The article analyzes the problem of stimulating students, having determined the essence of the rating system and, having analyzed educational success with the help of a rating system for monitoring and evaluating knowledge, it can be said with confidence that today the rating system for monitoring and evaluating knowledge provides a systematic, maximally stimulated work, as students as well as teachers. This is confirmed by the fact that the introduction of a rating system into the educational process creates the following benefits in learning: the stressful situation in the control process is reduced for both students and teachers; learning becomes student- centered; the rating system excludes any humiliation of the student's personality, allows him to evaluate his own abilities and capabilities, i.e. encourages him to conscientious work during the entire period of study.
The use of the project method in the educational process of the higher education institutions for students of historical specialties

The article is aimed at clarifying, through a questionnaire survey, certain practical aspects of the use of the project method in the organization of the educational process in higher education institutions. In the course of the study, the analytical and bibliographic method was applied to study the scientific literature on the use of the project method in the educational process. The authors of the study conducted an online questionnaire survey to clarify certain aspects of using the project method in teaching in HEIs practically. Based on the results of the study, the main most important theoretical aspects of the process of using the project method in HEIs for students of historical specialties have been highlighted. The point of view of students and teachers of HEIs on the key practical aspects of this issue has been investigated.
The use of pedagogical technologies for the organization of the educational process in institutions of higher education

Currently, the educational and training process is characterized by the search for such pedagogical technologies that would maximally contribute to forming a socially active, creative personality capable of independently regulating his life activities. The purpose of the academic paper is to study the theoretical fundamentals of the features of applying various pedagogical technologies. In the course of the present research, analysis, synthesis, induction and deduction were used to process scientific information. Based on the research results, the existing pedagogical technologies of organizing the educational process in the higher education sphere were studied from the perspective of their effectiveness.
Fundamentals of developing students’ cognitive abilities in european universities

The purpose of the academic paper is to study the fundamentals of developing students’ cognitive abilities in European universities. The methodology included a subject study to describe the fundamentals of developing students’ cognitive abilities based on analyzing secondary data and materials of the European Commission, the analysis of the cases of European universities’ two alliances: The 4EU+ European University Alliance (2023a) and The Arqus European University Alliance (2023a). The results indicate the development of quality inclusive education in the EU based on common human values and a partnership approach to cooperation between institutions and communities.
The Characteristics, Difficulties and Policy Suggestions of Chinese Education in South Korea

The Chinese language education in South Korea has a long history. The Chinese education in higher education has a history of more than 70 years. It has been 20 years since Chinese education was incorporated into the curriculum system of middle schools. The market for Chinese off-campus training has continued to grow. In 2004, the world
Alerting technologies to save lives in Forest Fires are effective with technology redundancies and multilingual CAP Event Terms basis. Design for worldwide consumer electronics adoption is preferable to reduce costs.

&amp;lt;p&amp;gt;California wild and forest fires in 2017 resulted in over 100 fatalities. While WEA alerts were transmitted to mobiles in selected areas, the power and network outages limited their delivery. WEA is similar to the SMS Broadcast system used elsewhere. It does not require subscription, and can be geotargeted, usually by map polygon. There are already available and in development other alerting technologies. The Emergency Alert System on radio and TV in the U.S. has been in use for many years. It is a broadcast break-in system the overrides program content. This was used in one location for the wildfires, but not elsewhere as geotargeting is not possible with this system. It is and analog broadcast technology architecture. AM and FM Broadcast in the U.S. now has HD Radio that is mixed analog and digital. A limited data message can be carried and used for selective delivery of messages. DAB, DAB+ and DRM also can carry a message payload, which can be used for a selective delivery mechanism when the receiver has location position. This may be in a vehicle radio/navigation system. The current digital television system in the U.S. and some other countries is now being replaced by ATSC 3.0. This provided a superior modulation format, Layered Division Multiplexing (LDM) for delivery of program content and alerts to suitable mobiles.&amp;amp;#160; An IC for UHF reception and prototype mobiles have been developed. No external antenna is required. Bothe of these new technologies are tested as delivering alerts independently of the mobile network. Within the limitations of radio and TV propagation, such capabilities would provide technology redundancy. The television signal propagation may be limited in rural areas, but ATSC 3.0 is capable of having on frequency repeaters to make a single frequency network for improved coverage of program content and alerting. Multilingual alerts based on the CAP Event Terms list with Message Formats are being provided for.&amp;lt;/p&amp;gt;
The effect of pH on heavy metals availability on crude oil remediated and non-remediated soil, Nkeleoken Community Eleme, Rivers State, Nigeria

This study was aimed at examining the influence of pH on heavy metals availability in polluted and non-polluted soils. The findings showed that metal availability depends on soil solution which is pH dependent. The values of pH in Nkeleoken remediated soil (RSENa, RSENb, RSENc) in dry season were 6.01, 5.87, 5.78 and 6.64, 5.77, 5.68 in wet season with mean values of 5.89±0.05 and 6.03±0.53 while the pH of the contaminated soil ranged between 4.31 – 4.41. The mean values of heavy metals in Nkeleoken remediated soil are; Se (318±9.88; 326±11.6 mg/kg), Cd (3.65; 3.82±1.09 mg/kg), V (1095±71.05; 1189±149 mg/kg), Ba (8.98±1.53; 9.40±1.89 mg/kg), Cu (8.19±0.08; 8.84±0.14 mg/kg), Ni (0.02±0.01 mg/kg), As (5.52±2.35; 10.3±2.38mg/kg), Pb (1.73±0.32; 2.70±1.38 mg/kg) and Cr (23.1±4.05; 23.1±4.05 mg/kg). The results revealed that V and Se had the highest concentration level followed by As while the least concentrations were observed in Cd, Ba, Pb and Cr in both dry and wet season. The concentrations of heavy metals in the non-remediated soil ranged from 866 – 868 mg/kg, 1356 – 6332 mg/kg, 211 – 218 mg/kg, 73.6 – 79.0, and 866 – 868 mg/kg for Se, V, Ba Pb and as in dry and wet seasons. However, Cr concentrations ranged from 6.03 – 7.02 mg/kg. The concentrations of V and Se in the non-remediated soils (NRSENx - NRSENz) during dry and wet seasons were higher than their corresponding in the remediated soils likewise the recommended values set by DPR (2012).
Online corporate communication: Should national culture matter?

Introduction The purpose of this paper is to investigate how the culture of a nation influences online corporate communication, focusing on the assessment of the culturability of business websites. Although the Internet constitutes a global phenomenon, cultural filters influence its use at the local level, ultimately determining a more or less favorable attitude toward a given website. Understanding and analysing the cultural adaptation of online communication is crucial as it has the potential to greatly influence how customers perceive and respond to the graphic and content elements. Methods Building upon Hofestede's and Hall's theories, a content analysis technique was used to code the cultural markers and new indicators have been created to allow the association of the graphic elements and the contents of the websites with the cultural characteristics. The countries chosen as case studies are India and Australia, which present significant cultural differences and allow highlighting of the practical implications deriving from the cultural adaptation of the website design. Results The results of this paper show that the company websites analyzed are designed to incorporate the cultural nuances of the target country. In particular, on the Australian websites, there is a greater frequency of cultural markers referring to individualism, masculinity, and uncertainty avoidance compared to Indian ones. On the contrary, Indian websites show more cultural markers referring to power distance, high context, and polychronic characteristics than Australian ones. This paper overcomes the qualitative approaches of the previous literature, developing new indicators for website analysis and providing a quantitative comparison with Hofstede and Hall frameworks. Discussion This work provides a guideline for companies, analysts, and the different professionals involved in online communication and web design. More specifically, they have to be aware of the cultural challenges when they operate outside the national boundaries, by designing a culturally usable website in one of the countries chosen for this study.
Towards Robust Underwater Image Enhancement

Underwater images often suffer from blurring and color distortion due to absorption and scattering in the water. Such effects are undesirable since they may hinder computer vision tasks. Many underwater image enhancement techniques have been explored to address this issue, each to varying degrees of success. The large variety of distortions in underwater images is difficult to handle by any singular method. This study observes four underwater image enhancement methods, i.e., Underwater Light Attenuation Prior (ULAP), statistical Background Light and Transmission Map estimation (BLTM), and Natural-based Underwater Image Color Enhancement (NUCE), and Global–Local Networks (GL-Net). These methods are evaluated on the Underwater Image Enhancement Benchmark (UIEB) dataset using quantitative metrics, e.g., SSIM, PSNR, and CIEDE2000 as the metrics. Additionally, a qualitative analysis of image quality attributes is also performed. The results show that GL-Net achieves the best enhancement result, but based on the qualitative assessment, this method still has room for improvement. A proper combination between the non-learning-based component and learning-based component should be investigated to further improve the robustness of the method.
Oracle-SAGE: Planning Ahead in Graph-Based Deep Reinforcement Learning

Deep reinforcement learning (RL) commonly suffers from high sample complexity and poor generalisation, especially with high-dimensional (image-based) input. Where available (such as some robotic control domains), low dimensional vector inputs outperform their image based counterparts, but it is challenging to represent complex dynamic environments in this manner. Relational reinforcement learning instead represents the world as a set of objects and the relations between them; offering a flexible yet expressive view which provides structural inductive biases to aid learning. Recently relational RL methods have been extended with modern function approximation using graph neural networks (GNNs). However, inherent limitations in the processing model for GNNs result in decreased returns when important information is dispersed widely throughout the graph. We outline a hybrid learning and planning model which uses reinforcement learning to propose and select subgoals for a planning model to achieve. This includes a novel action selection mechanism and loss function to allow training around the non-differentiable planner. We demonstrate our algorithms effectiveness on a range of domains, including MiniHack and a challenging extension of the classic taxi domain.
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection Using Compounded Corruptions

Modern deep neural network models are known to erroneously classify out-of-distribution (OOD) test data into one of the in-distribution (ID) training classes with high confidence. This can have disastrous consequences for safety-critical applications. A popular mitigation strategy is to train a separate classifier that can detect such OOD samples at test time. In most practical settings OOD examples are not known at train time, and hence a key question is: how to augment the ID data with synthetic OOD samples for training such an OOD detector? In this paper, we propose a novel Compounded Corruption (CnC) technique for the OOD data augmentation. One of the major advantages of CnC is that it does not require any hold-out data apart from training set. Further, unlike current state-of-the-art (SOTA) techniques, CnC does not require backpropagation or ensembling at the test time, making our method much faster at inference. Our extensive comparison with 20 methods from the major conferences in last 4 years show that a model trained using CnC based data augmentation, significantly outperforms SOTA, both in terms of OOD detection accuracy as well as inference time. We include a detailed post-hoc analysis to investigate the reasons for the success of our method and identify higher relative entropy and diversity of CnC samples as probable causes. Theoretical insights via a piece-wise decomposition analysis on a two-dimensional dataset to reveal (visually and quantitatively) that our approach leads to a tighter boundary around ID classes, leading to better detection of OOD samples.
Robust Prototypical Few-Shot Organ Segmentation With Regularized Neural-ODEs

Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a small amount of annotation is needed for generalisation to novel classes. This is especially seen in medical domains where dense pixel-level annotations are expensive to obtain. In this paper, we propose Regularized Prototypical Neural Ordinary Differential Equation (R-PNODE), a method that leverages intrinsic properties of Neural-ODEs, assisted and enhanced by additional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cluster</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistency</i> losses to perform Few-Shot Segmentation (FSS) of organs. R-PNODE constrains support and query features from the same classes to lie closer in the representation space thereby improving the performance over the existing Convolutional Neural Network (CNN) based FSS methods. We further demonstrate that while many existing Deep CNN-based methods tend to be extremely vulnerable to adversarial attacks, R-PNODE exhibits increased adversarial robustness for a wide array of these attacks. We experiment with three publicly available multi-organ segmentation datasets in both in-domain and cross-domain FSS settings to demonstrate the efficacy of our method. In addition, we perform experiments with seven commonly used adversarial attacks in various settings to demonstrate R-PNODE’s robustness. R-PNODE outperforms the baselines for FSS by significant margins and also shows superior performance for a wide array of attacks varying in intensity and design.
The Impact of Twitter Spaces in Speaking Skill for EFL Students

Developing online media for teaching is needed in order to engage a technology and a strategy in teaching. Twitter Spaces is a part of Twitter that the user could make a room and start discussion. Thus, this research conducted to see the impact of using this app in teaching speaking skill. This research used basic qualitative method. The participants are the first grade of Junior High School students. The result is there are obstacles in using Twitter Spaces as the media of learning English; the students never used this app before, Twitter Spaces limited the participants in the room, and a new link must be made when the host’s connection is unstable. The students felt comfortable using Twitter Spaces in speaking English because they don’t need to show their face. But there is no significant impact to them.
The Implementation of C4.5 Algorithm for Determining the Department of Vocational High School

The selection of departments in vocational high schools (SMK) is a must for students for determining the concentration of student learning interest for three years in a school. Lack of student knowledge and outreach about this department caused many students choose their majors by the most choices and following other students. These problem can cause some difficulties for the students to participate in learning and most of them are failed. Every student needs to select their major based on their interests, abilities, and talents. Because every student has different ability and talent. C4.5 algorithm is an algorithm used to construct a decision tree. C4.5 algorithm used to determine the majors at vocational high school to be taken by students according to the background of some criterias such as mathematics score, English score, interests and talents. The results of this study are the determination of the departments with the accuracy of the calculation using the confusion matrix method with a 98,55% accuracy rate and 100% recall rate value.
Transfer Learning for the Visual Arts: The Multi-modal Retrieval of Iconclass Codes

Iconclass is an iconographic thesaurus, which is widely used in the digital heritage domain to describe subjects depicted in artworks. Each subject is assigned a unique descriptive code, which has a corresponding textual definition. The assignment of Iconclass codes is a challenging task for computational systems, due to the large number of available labels in comparison to the limited amount of training data available. Transfer learning has become a common strategy to overcome such a data shortage. In deep learning, transfer learning consists in fine-tuning the weights of a deep neural network for a downstream task. In this work, we present a deep retrieval framework, which can be fully fine-tuned for the task under consideration. Our work is based on a recent approach to this task, which already yielded state-of-the-art performance, although it could not be fully fine-tuned yet. This approach exploits the multi-linguality and multi-modality that is inherent to digital heritage data. Our framework jointly processes multiple input modalities, namely, textual and visual features. We extract the textual features from the artwork titles in multiple languages, whereas the visual features are derived from photographic reproductions of the artworks. The definitions of the Iconclass codes, containing useful textual information, are used as target labels instead of the codes themselves. As our main contribution, we demonstrate that our approach outperforms the state-of-the-art by a large margin. In addition, our approach is superior to the M 3 P feature extractor and outperforms the multi-lingual CLIP in most experiments due to the better quality of the visual features. Our out-of-domain and zero-shot experiments show poor results and demonstrate that the Iconclass retrieval remains a challenging task. We make our source code and models publicly available to support heritage institutions in the further enrichment of their digital collections.
The Influence of Language Identity on Students’ Learning Foreign Language Process

Everyone has an identity and language is a sign of that. The associations in a person's environment, with whom they hang out, and where they learn can all have an impact on their identities. This research aims to explain the extent to which language is still used as a symbol of human identity. The language used every day can be a source of pride. Evidence of this pride can be seen from the development of its use which is dynamic. Language has a very important function to express cultural treasures. However, language over time has also developed. Challenges in learning a foreign language can also be influenced by the use of a person's everyday language. Students have their challenges in learning a foreign language, therefore it can be seen whether the everyday language they use has an influence when they learn a foreign language.
Hybrid Algorithm for Resource Aware Predictive Scheduling: A case-study to Human Activity Recognition

Analyzing big stream data is a significant task for real-time high-voluminous data. Various research works have evolved to analyze those big stream data. However, state-of-the-art systems have certain limitations, such as scheduling time (ST), resource-aware predictive scheduling efficiency (RAPSE), memory consumption (MC), false positive rate (FPR), etc. This paper proposes a hybrid algorithm for predictive resource-aware scheduling to address the issues. The algorithm integrates Elastic-net regularization with Kernelized Fisher Discriminant (KFD) and Map Reduce classification process for a case study on a high volume of human activity recognition (HAR) dataset. The Elastic-net regularization process selects significant features from the dataset and adds certain performance measures for efficient scheduling by the KFD process. The Map Reduce classifier classifies the data streams assigned to a respective processor by the KFD to achieve a low false positive rate. Experimentation has been validated with state-of-the-art systems using standard challenges such as RAPSE, MC, FPR, and ST. Results have also proven that the proposed system has certain merits in implementation over the other state-of-the-art systems in realtime human activity recognition.
Abstract Extraction Algorithm on Account of Parallel Corpus in English Teaching

Abstract extraction of traditional English teaching texts usually has some problems such as incomplete resource integration and poor resource scheduling. In view of this situation, this paper uses parallel corpus to improve the extraction model of text abstract in English teaching, and improves the relevant modules of original text abstract in English teaching, thus effectively improving the extraction effect of text abstract. In this algorithm, association rules are adopted to process the abstracts, analyze the features of the samples, extract the features of English short sentences and sentences, and finally improve the accuracy of text extraction. This paper studies the method of parallel corpus, improves the data model of text abstract extraction in English teaching, and reveals the working principle and mechanism of text abstract extraction in English teaching. The data test shows that using parallel corpus to create a text abstract extraction model in English teaching has excellent performance in the extraction of text abstract in English teaching.
Research on Information Visualization of Thermal Power Plant Based on D3+SVG Technology

Under the background of huge amount of data information, isolated data information and low utilization of data information in thermal power plants, this paper proposes to develop an information visualization platform based on B/S structure MVC framework with the help of information visualization theoretical knowledge research and the use of D3, SVG and other technologies. This platform has a high level of interactivity and extensibility. It not only performs the power plant data information visualization function, but it also meets the requirements of the user-defined visualization scheme.