Reviewerly/exHarmony huggingface dataset:
Analysis of market indicators and prospects for online education development in segment of massive open online courses

The article presents the results of a study of the dynamics of the online education market in the segment of massive open online courses (MOOCs), on the basis of which it was concluded that citizens of various countries are highly interested in these resources and the prospects for its further growth. Despite the significant annual growth of the global e-learning market, it is the COVID 19 pandemic and the further transition of universities to distance learning that have become the main growth drivers for the MOOC segment. The results of a number of studies made it possible to conclude that today, both among students and teachers, there are not only positive, but also negative reviews about the practice of using MOOCs in the educational process of universities, which is confirmed by statistics - a low level of student involvement in course topics, as well as low completion rates for massive online courses. The relevance of the use of neuromarketing tools for conducting cognitive research in order to obtain information about the neurophysiological reactions that occur in students in the course of online learning using MOOCs, which is necessary to improve its efficiency and effectiveness, is substantiated. Neuromarketing tools, including eyetracking, make it possible to monitor the behavior of students when using online platforms in order to identify their reactions to the content of educational content and determine the next directions for improving MOOC elements, and as a result, increasing student motivation, involvement and academic performance.
Exploring the Use of Austin’s Theory of Illocutionary Acts in Different Social Interactions

This paper reviews how Austin’s illocutionary act theory is used in face-to-face communication and online communication. In the aspect of face-to-face interaction, this paper places attention on the use of polite language and interaction in ESL (English as a second language) classrooms. In terms of online interaction, this paper explored interaction on Facebook and the use of memes, emoticons, emojis and stickers. After reviewing several research, this paper concluded that when talking face-to-face, the level of formality, gender, and cultural background affects people’s polite language. In ESL classrooms, the power, distance and rate of imposition affect teachers’ and students’ choice of language. However, the result of speech acts preference on Facebook is conflict. Previous research pointed out that memes can be categorized as communicative speech acts. Evidence shows that positive emoticons are preferred, which can improve friendships and vice versa. The review of literature contributes to further studies on the illocutionary act analysis of ESL classrooms in China and the communicative effectiveness of memes on the receivers.
Optimizing student engagement in edge-based online learning with advanced analytics

Edge-Based Online Learning (EBOL), a technique that combines the practical, hands-on approach of EBOL with the convenience of Online Learning (OL), is growing in popularity. But accurately monitoring student engagement to enhance teaching methodologies and learning outcomes is one of the difficulties of OL. To determine this challenge, this paper has put forth an Edge-Based Student Attentiveness Analysis System (EBSAAS) method, which uses a Face Detection (FD) algorithm and a Deep Learning (DL) model known as DLIP to extract eye and mouth landmark features. Images of the eye and mouth are used to extract landmarks using DLIP or Deep Learning Image Processing. Landmark Localization pre-trained models for Facial Landmark Localization (FLL) are one well-liked DL model for facial landmark recognition. The Visual Geometry Group-19 (VGG-19) learning model then uses these features to classify the student's level of attentiveness as fatigued or focused. Compared to a server-based model, the proposed model is developed to execute on an Edge Device (ED), enabling a swift and more effective analysis. The EBOL achieves 95.29% accuracy and attains 2.11% higher than existing model 1 and 4.41% higher than existing model 2. The study's findings have shown how successful the proposed method is at assisting teachers in changing their teaching methodologies to engage students better and enhance learning outcomes.
Toward Constructing a Continuous Logical Operator for Error-Corrected Quantum Sensing

Error correction has long been suggested to extend the sensitivity of quantum sensors into the Heisenberg Limit.However, operations on logical qubits are only performed through universal gate sets consisting of finite-sized gates such as Clifford + T.Although these logical gate sets allow for universal quantum computation, the finite gate sizes present a problem for quantum sensing, since in sensing protocols, such as the Ramsey measurement protocol, the signal must act continuously.The difficulty in constructing a continuous logical operator comes from the Eastin-Knill theorem, which prevents a continuous signal from being both fault-tolerant to local errors and transverse.Since error correction is needed to approach the Heisenberg Limit in a noisy environment, it is important to explore how to construct fault-tolerant continuous operators.In this paper, a protocol to design continuous logical z-rotations is proposed and applied to the Steane Code.The fault tolerance of the designed operator is investigated using the Knill-Laflamme conditions.The Knill-Laflamme conditions indicate that the diagonal unitary operator constructed cannot be fault tolerant solely due to the possibilities of X errors on the middle qubit.The approach demonstrated throughout this paper may, however, find success in codes with more qubits such as the Shor code, distance 3 surface code, [15, 1, 3] code, or codes with a larger distance such as the [11, 1, 5] code.
Strategies for translating machine errors in automatically generated texts (using GPT-4 as an example)

The article discusses the strategies of translation of «machine texts» on the example of generative transformers (GPT). Currently, the study and development of machine text generation has become an important task for processing and analyzing texts in different languages. Modern technologies of artiﬁcial intelligence and neural networks allow us to create powerful tools for activities in this ﬁeld, which are becoming more and more effective every year. Generative transformers are one of such tools. The study of generative transformers also allows developers to create more accurate and efﬁcient machine translation algorithms, which improves the quality of translations and improves the user experience. In this context, the features of machine texts created by generative transformers, their patterns, errors and imperfections, which require special translation strategies, deserve special interest. Today we can say that the generation of unique and relevant texts is a routine task that has been automated. Nevertheless, certain restrictions for the use of such texts still exist, in particular, their use requires the use of appropriate translation strategies. The paper proposes the author’s typology of translation strategies, where, taking into account the features of AST, it is proposed to add a substrategy of tertiary-moderation translation.
Odmiana medialna języka a ustne wypowiedzi uczniów

This article seeks to answer the question whether features of media language can be discerned in the utterances of the younger generation and, if so, which ones. The oral presentations performed during the secondary school final examinations(obtained from the recordings) were studied. They have never been analysed in this respect before. The presentations have, on the one hand, an official character and, on the other, are spontaneous as they are produced on an ad hoc and continuousbasis, with no possibility of predicting their final shape. First, the article reviews the characterisation of media language by researchers working on the subject in order to establish a group of the most distinctive features that may be present in students’ expressions. An analysis of the material carried out at the cognitive, structural, and linguistic levels is then presented, which has made it possible to discern the presence of certain features of the media variety of language, including superficiality and fragmentation, definiteness, associativeness, and colloquiality. The paper is concluded with a statement that the changes that are taking place in the language (and therefore in the thinking) of the younger generation are becoming a challenge for educational didactics, which should provide contemporary schools with more effective models of language education, taking into account thecurrent expansion of media literacy.
Efficient generation of text feedback in object-oriented programming education using cached performer revision

Transformer, a deep learning technology, has achieved great success in various applications such as natural language processing. However, its space–time consumption remains a concern, leading to the issue of AI technology monopoly. Therefore, downsizing this technology is critical to mitigating the adverse effects of technological monopoly on innovation and competition. This article proposes a revised version of the Performer, an efficient variant of Transformer. Specifically, the proposed revision aims to resolve three issues associated with Performer. First, generating a target token necessitates padding tokens that require O(L) space and time for sequences of maximum length L. Second, Performer redundantly calculate attentions between previously generated tokens. Third, inconsistencies arise between the training and inference phases due to normalizer calculation in the stable FAVOR+ masked attention operation. The proposed revision introduces a cached version of the FAVOR+ operation, resulting in fast text generation with O(L) time and O(1) space complexity. To examine the effectiveness of the proposed revision, a Performer-based encoder/decoder model that generates text feedback for code correction in object-oriented programming education is developed and evaluated. The results show that the revised Performer achieves high accuracy and a more than 120-fold increase in inference speed. In addition, user evaluation shows that the feedback generated by this model is more beneficial for programming education than that generated by ChatGPT.
Document-Level Relation Extraction with Local Relation and Global Inference

The current popular approach to the extraction of document-level relations is mainly based on either a graph structure or serialization model method for the inference, but the graph structure method makes the model complicated, while the serialization model method decreases the extraction accuracy as the text length increases. To address such problems, the goal of this paper is to develop a new approach for document-level relationship extraction by applying a new idea through the consideration of so-called “Local Relationship and Global Inference” (in short, LRGI), which means that we first encode the text using the BERT pre-training model to obtain a local relationship vector first by considering a local context pooling and bilinear group algorithm and then establishing a global inference mechanism based on Floyd’s algorithm to achieve multi-path multi-hop inference and obtain the global inference vector, which allow us to extract multi-classified relationships with adaptive thresholding criteria. Taking the DocRED dataset as a testing set, the numerical results show that our proposed new approach (LRGI) in this paper achieves an accuracy of 0.73, and the value of F1 is 62.11, corresponding to 28% and 2% improvements by comparing with the classical document-level relationship extraction model (ATLOP), respectively.
What is in the paragraphs of various sections of research articles?

This paper strives to uncover the leading paragraph build-up patterns in academic writing. It employs the framework pioneered by Mathesius (1942/1982) and Daneš (1994, 1995), and elaborated on by Pípalová (2005, 2008a, 2008b, 2014). The corpus assembled for the study involves three distinct sections of Research Articles, viz. Abstracts, Introductions and Conclusions. The research confirms the general prevalence of Broad P-theme paragraphs, especially the Content Frame categories. It also demonstrates that non-canonical (i.e. transitional and peripheral) forms of paragraph build-up tend to prevail and identifies a number of factors at play. The paper also shows that the distribution of paragraph patterns is not homogeneous and appears to change across the space of the Research Articles.
Incorporating Uncertainty Quantification for the Performance Improvement of Academic Recommenders

Deep learning is widely used in many real-life applications. Despite their remarkable performance accuracies, deep learning networks are often poorly calibrated, which could be harmful in risk-sensitive scenarios. Uncertainty quantification offers a way to evaluate the reliability and trustworthiness of deep-learning-based model predictions. In this work, we introduced uncertainty quantification to our virtual research assistant recommender platform through both Monte Carlo dropout ensemble techniques. We also proposed a new formula to incorporate the uncertainty estimates into our recommendation models. The experiments were carried out on two different components of the recommender platform (i.e., a BERT-based grant recommender and a temporal graph network (TGN)-based collaborator recommender) using real-life datasets. The recommendation results were compared in terms of both recommender metrics (AUC, AP, etc.) and the calibration/reliability metric (ECE). With uncertainty quantification, we were able to better understand the behavior of our regular recommender outputs; while our BERT-based grant recommender tends to be overconfident with its outputs, our TGN-based collaborator recommender tends to be underconfident in producing matching probabilities. Initial case studies also showed that our proposed model with uncertainty quantification adjustment from ensemble gave the best-calibrated results together with the desirable recommender performance.
mapSR: A Deep Neural Network for Super-Resolution of Raster Map

The purpose of multisource map super-resolution is to reconstruct high-resolution maps based on low-resolution maps, which is valuable for content-based map tasks such as map recognition and classification. However, there is no specific super-resolution method for maps, and the existing image super-resolution methods often suffer from missing details when reconstructing maps. We propose a map super-resolution (mapSR) model that fuses local and global features for super-resolution reconstruction of low-resolution maps. Specifically, the proposed model consists of three main modules: a shallow feature extraction module, a deep feature fusion module, and a map reconstruction module. First, the shallow feature extraction module initially extracts the image features and embeds the images with appropriate dimensions. The deep feature fusion module uses Transformer and Convolutional Neural Network (CNN) to focus on extracting global and local features, respectively, and fuses them by weighted summation. Finally, the map reconstruction module uses upsampling methods to reconstruct the map features into the high-resolution map. We constructed a high-resolution map dataset for training and validating the map super-resolution model. Compared with other models, the proposed method achieved the best results in map super-resolution.
Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach

The use of Generative Adversarial Networks (GANs) has led to significant advancements in the field of compositional image synthesis. In particular, recent progress has focused on achieving synthesis at the semantic part level. However, to enhance performance at this level, existing approaches in the literature tend to prioritize performance over efficiency, utilizing separate local generators for each semantic part. This approach leads to a linear increase in the number of local generators, posing a fundamental challenge for large-scale compositional image synthesis at the semantic part level. In this paper, we introduce a novel model called Single-Generator Semantic-Style GAN (SSSGAN) to improve efficiency in this context. SSSGAN utilizes a single generator to synthesize all semantic parts, thereby reducing the required number of local generators to a constant value. Our experiments demonstrate that SSSGAN achieves superior efficiency while maintaining a minimal impact on performance.
Fine-Grained Image Recognition by Means of Integrating Transformer Encoder Blocks in a Robust Single-Stage Object Detector

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is both minimal and substantial intra-class variance. Current methods address the issue through first locating selective regions with region proposal networks (RPNs), object localization, or part localization, followed by implementing a CNN network or SVM classifier to those selective regions. This approach, however, makes the process simple via implementing a single-stage end-to-end feature encoded with a localization method, which leads to improved feature representations of individual tokens/regions through integrating the transformer encoder blocks into the Yolov5 backbone structure. These transformer encoder blocks, with their self-attention mechanism, effectively capture global dependencies and enable the model to learn relationships between distant regions. This improves the model’s ability to understand context and capture long-range spatial relationships in an image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative feature maps from transformer encoder blocks. We established the potential of the single-stage detector for the fine-grained image recognition task, achieving state-of-the-art 93.4% accuracy, as well as outperforming existing one-stage recognition models. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.
From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies

In this paper, we propose a novel approach for obtaining predictions from per-class scores to improve the accuracy of multi-label classification systems. In a multi-label classification task, the expected output is a set of predicted labels per each testing sample. Typically, these predictions are calculated by implicit or explicit thresholding of per-class real-valued scores: classes with scores exceeding a given threshold value are added to a prediction set. In our work, we propose a neural network-based thresholding phase for multi-label classification systems and examine its influence on the overall classification performance measured by micro- and macro-averaged F1 scores on synthetic and real datasets. In contrast to classic thresholding methods, our approach has the unique property of being able to recover from scoring errors, because each decision about a given label prediction depends on the corresponding class score, as well as on all the other class scores for a given sample at once. The method can be used in combination with any classification system that outputs real-valued class scores. The proposed thresholding methods are trained offline, after the completion of the scoring phase. As such, it can be considered a universal fine-tuning step that can be employed in any multi-label classification system that seeks to find the best multi-label predictions based on class scores. In our experiments on real datasets, the input class scores were obtained from two third-party baseline classification systems. We show that our approach outperforms the traditional thresholding methods, which results in the improved performance of all tested multi-label classification tasks. In terms of relative improvement, on real datasets, the micro-F1 score is higher by up to 40.6%, the macro-F1 score is higher by up to 3.6%, and the averaged micro–macro-F1 score is higher by up to 30.1%, considering single models only. We show that ensembles and hybrid models give even better results. We show examples of successful extreme recoveries, where the system, equipped with our method, was able to correctly predict labels, which were highly underscored after the scoring phase.
Pandemic Innovations in Teacher Education: Communities of Practice, Mentoring, and Technology

This study aimed to investigate the transformation of pre-service teaching experience due to virtual or hybrid completion during the 2020–2021 school year and to identify teaching and mentoring innovations that teacher educators should continue to promote. The research involved 14 student teachers and 5 mentor teachers from the United States across elementary, secondary, and pre-K–12 programs who participated in surveys, semi-structured interviews, and focus groups. The results indicated that the integration of technology in student teaching and the shift to virtual or hybrid learning brought about new challenges and opportunities for both student teachers and mentor teachers. The study highlights technology that may continue to be used post-pandemic, the promotion of virtual communities of practice, and ways to quickly integrate and maximize student teachers in the classroom.
A High Performance Reconfigurable Hardware Architecture for Lightweight Convolutional Neural Network

Since the lightweight convolutional neural network EfficientNet was proposed by Google in 2019, the series of models have quickly become very popular due to their superior performance with a small number of parameters. However, the existing convolutional neural network hardware accelerators for EfficientNet still have much room to improve the performance of the depthwise convolution, squeeze-and-excitation module and nonlinear activation functions. In this paper, we first design a reconfigurable register array and computational kernel to accelerate the depthwise convolution. Next, we propose a vector unit to implement the nonlinear activation functions and the scale operation. An exchangeable-sequence dual-computational kernel architecture is proposed to improve the performance and the utilization. In addition, the memory architectures are designed to complete the hardware accelerator for the above computing architecture. Finally, in order to evaluate the performance of the hardware accelerator, the accelerator is implemented based on Xilinx XCVU37P. The results show that the proposed accelerator can work at the main system clock frequency of 300 MHz with the DSP kernel at 600 MHz. The performance of EfficientNet-B3 in our architecture can reach 69.50 FPS and 255.22 GOPS. Compared with the latest EfficientNet-B3 accelerator, which uses the same FPGA development board, the accelerator proposed in this paper can achieve a 1.28-fold improvement of single-core performance and 1.38-fold improvement of performance of each DSP.