Reviewerly/exHarmony huggingface dataset:
The Influence of a Reading Setup on Digital Reading Strategies in High School Students: Results of an Eye-Tracking Study

&lt;p style="text-align: right;"&gt;The paper aims to investigate the correlation between reading setup and digital reading strategies used by Russian schoolchildren. To study the influence of the reading setup factor, we conducted an eye-tracking study of high school students (N=66), which showed the dependence of digital reading strategies on the pre-reading task by experienced readers. The study showed that the pattern of skimming digital texts does not always appear explicitly, and revealed a significant influence on reading strategies of both reader's individual differences and the rhetorical organization of the text, as well as the reading task factor. The task of searching for specific information led to a more thorough primary reading of the entire text with fewer returns and transitions between the other semantic elements of the digital educational text. In the case of the text analysis task, the first reading was more cursory, but there was more jumping back and forth between paragraphs to establish the logical linkage of the text information. The influence of the reading setup on the digital reading strategy illustrates the importance of formulating the reading task correctly in order to ensure an effective digital reading experience.&lt;/p&gt;
Heterogeneous Graph Neural Network Knowledge Graph Completion Model Based on Improved Attention Mechanism

The main purpose of knowledge graph completion is to predict the missing part of the triple. Through learning from many existing models, we found that whether it is a convolutional neural network model or a translation model, when dealing with triples, they independently treat triples and ignore the potential rich semantics and hidden information in the neighborhood of triples. Although some graph neural network models can use the structural characteristics of graph connectivity, they have not fully considered the heterogeneous graph containing different types of entities and relationships, and have updated a lot of information for the central entity. This article aims to propose a knowledge graph completion model with Improved Attention mechanism for Heterogeneous Graph Neural networks (IAHGN), which mainly uses the graph structure characteristics of heterogeneous graphs, and updates the feature representation of central entities by Adding Hierarchical Attention Mechanism (AHAM). Hierarchical attention includes entity-level attention and semantic-level attention, and then Conv-transE named convolutional network is used as decoder. Firstly, AHAM can aggregate neighbor features of different meta-paths through entity-level attention, while semantic-level attention can distinguish the importance of different meta-paths. Then AHAM can update the feature representation of central entities by aggregating neighbor features of meta-paths in a hierarchical way. Finally, the improved decoder Conv-transE can keep the translation characteristics between relations and entities, and achieve better link prediction performance. Through experiments, we prove that IAHGN proposed in this paper is effective on standard FB15k-237 and WN18RR datasets, and has relative improvement in Hits@10 and MRR values compared with other models.
Target-Oriented Sentiment Classification with Sequential Cross-Modal Semantic Graph

Multi-modal aspect-based sentiment classification (MABSC) is an approach aimed at classifying the sentiment of a target entity mentioned in a sentence using images. However, previous methods failed to account for the fine-grained semantic association between the image and the text, which resulted in limited identification of fine-grained image aspects and opinions. To address these limitations, a new approach called SeqCSG has been proposed in this paper. SeqCSG enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs. SeqCSG utilizes image captions and scene graphs to extract both global and local fine-grained image information and considers them as elements of the cross-modal semantic graph along with tokens from tweets. The sequential cross-modal semantic graph is represented as a sequence with a multi-modal adjacency matrix indicating relationships between elements. Experimental results have shown that the approach outperforms existing methods and achieves state-of-the-art performance on two standard datasets. Further analysis has demonstrated that the model can implicitly learn the correlation between fine-grained information of the image and the text with the given target.
Semantic and Frequency Representation Mining for Face Manipulation Detection

Face manipulation technologies pose a great threat to the current digital media. Although previous methods have achieved excellent detection performance, they tend to focus on specific artifacts and lead to overfitting. Erasing-based augmentations can alleviate this issue, but they still suffer from high randomness and fixed shapes. Therefore, we propose a novel face masking method named Landmarks Based Erasing (LBE), which exploits the geometric information of the face and forgery attention map to perform erasure, thereby forcing the network to mine discriminative features from other face regions. Furthermore, Wavelet Packet with Attention (WPA) mechanism module is designed to extract multi-level frequency features, providing a complementary perspective to LBE module. Finally, we employ a score fusion strategy to fuse two types of complementary feature information for forgery detection. Extensive experiments on three large public datasets demonstrate that our proposed method achieves state-of-the-art detection performance and exhibits good generalization ability.
Water Conservancy Remote Sensing Image Classification Based on Target-Scene Deep Semantic Enhancement

Water conservancy remote sensing image classification is an important task for water conservancy image interpretation, which provides indispensable analysis results for the applications of water conservancy remote sensing images. However, in the high-resolution remote sensing images of water conservancy, the objects and water bodies are usually diverse, and the image semantics are fuzzy, leading to poor classification performance. To handle it, this paper proposes a novel classification method based on target-scene deep semantic enhancement for high-resolution remote sensing images of water conservancy, which consists of two key branches. The upper branch is an improved ResNet18 network based on dilated convolution, which is used to extract scene-level features of images. The lower branch is a novel multi-level semantic-understanding based Faster R-CNN, which is used to extract the target-level features of images. The experimental results show that the features extracted by the proposed method contain more discriminative scene-level information as well as more detailed target-level information, which can effectively help generate better classification performance.
Diffusion Policies as Multi-Agent Reinforcement Learning Strategies

In the realm of multi-agent systems, the application of reinforcement learning algorithms frequently confronts distinct challenges rooted in the non-stationarity and intricate nature of the environment. This paper presents an innovative methodology, denoted as Multi-Agent Diffuser (MA-Diffuser), which leverages diffusion models to encapsulate policies within a multi-agent context, thereby fostering efficient and expressive inter-agent coordination. Our methodology embeds the action-value maximization within the sampling process of the conditional diffusion model, thereby facilitating the detection of optimal actions closely aligned with the behavior policy. This strategy capitalizes on the expressive power of diffusion models, while simultaneously mitigating the prevalent function approximation errors often found in offline reinforcement learning environments. We have validated the efficacy of our approach within the Multi-Agent Particle Environment, and envisage its future extension to a broader range of tasks.
Text Semantic Matching Research Based on Parallel Dropout

Text semantic matching is a fundamental task in natural language understanding, and has a wide range of applications in information retrieval, question and answer systems, reading comprehension, and machine translation. Currently, a better way to do text semantic matching tasks is to extract word vectors or sentence vectors with BERT and then fine-tuning them, but the commonly used fine-tuning methods suffer from the overfitting problem. We propose a bi-directional long short-term memory-parallel dropout model (BiLSTM-PD), which combines word vectors and sentence vectors to improve feature vectors quality and uses parallel dropout to reduce overfitting. First, word vectors and sentence vectors are generated using the pre-trained model, BiLSTM converts the word vectors into sentence vectors, and the sentence vectors generated by BiLSTM are combined with the sentence vectors generated by the pre-trained model to form the final sentence vectors representation; then, four dropout functions are used to randomly discard a portion of the neurons of the sentence vectors to obtain four subsets, and then a linear layer is used to transform the four subsets of dimensionality and calculate the average value, and then use the Softmax and Argmax functions to calculate the predicted value of each batch to know whether the two sentences are similar. Experiments on two text semantic matching datasets and detailed analyses demonstrate the effectiveness of our model.
Boosting Adversarial Transferability Through Intermediate Feature

Deep neural networks are well known to be vulnerable to adversarial samples in the white-box setting. However, as research progressed, researchers discovered that adversarial samples can perform black-box attacks, that is, adversarial samples generated on the original model can cause models with different structures from the original model to misidentify. A large number of methods have recently been proposed to improve the transferability of adversarial samples, but the majority of them have low transferability. In this paper, we propose an intermediate feature-based attack algorithm to improve the transferability of adversarial samples even further. Rather than generating adversarial samples directly from the original samples, we continue to optimize existing adversarial samples to improve attack transferability. To begin, we calculate the feature importance of the original samples using existing adversarial samples. Then, we analyze which features are more likely to produce adversarial samples with high transferability. Finally, we optimize those features to improve the attack transferability of the adversarial samples. Furthermore, rather than using the model’s logit output, we generate adversarial samples using the model’s intermediate layer output. Extensive experiments on the standard ImageNet dataset show that our method improves transferability and outperforms state-of-the-art methods.
Communicative Competence in the Structure of "Soft Skills" as Part of the University Students Professional Training

&lt;p&gt;Modern society transformation stimulates the search for new self-fulfillment conditions of a professional&amp;rsquo;s personality. Soft skills, acquired through targeted professional training, are one of the determinants of professional activity growth. Based on the analysis of domestic and foreign literature, in accordance with the Russian Managers Association terminology, the article lists specific communicative competencies of a "successful" manager. As a hypothesis of the study, an assumption was made about the presence of a predominantly low development level of communicative competencies that are included in the structure of students&amp;rsquo; professionally significant "soft skills". The study (N=184) involved students aged 18&amp;ndash;19 (M=18,28; SD=0,45), including 110 female and 74 male students of the 1st year bachelor Management program from Nizhny Novgorod universities. The following methods were used: the &amp;ldquo;Level of subjective control&amp;rdquo; by E.F. Bazhin, E.A. Golynkin, L.M. Etkind; "Emotional intelligence test" by D.V. Lucin; "Сommunicative social competence diagnosis" by N.P. Fetiskin, V.V. Kozlov, G.M. Manuilov. The diagnostic results showed the predominant presence of medium and high development levels of communicative competencies, related to communicative social competence, emotional intelligence and subjective control of future managers. The presence of a medium and high levels of the studied communicative competencies development allows using them as a basis for the successful &amp;ldquo;soft skills&amp;rdquo; development of a management specialist.&lt;/p&gt;
PELATIHAN PERBAIKAN KIPAS ANGIN DI DESA KARANGSEGAR

The aim of the fan repair training in Karangsegar Village is to increase the community's knowledge and skills in fan repair, reduce the cost of community spending in buying new fans and increase community awareness in repairing damaged electronic goods rather than buying new ones straight away. The service method used is a training method targeting the youth of Karangsegar Village. The results of the service showed that participants experienced an increase in understanding of fan repair with an average value of 78% and participants were able to provide understanding to their closest relatives regarding fan repair.
READINESS OF THE FUTURE TEACHER FOR INNOVATIVE ACTIVITIES

The article substantiates the essence and content of future teacher's readiness for innovative activity in higher pedagogical school. Contemporary requirements for the professional training of future teachers are presented in the context of the future tea
Situation-Aware Framework for Energy Saving in Unattended Activities of Daily Living in Smart Home Environments

Abstract Activities of daily living (ADLs) are basic self-care tasks that are necessary for independent living. It is also used to assess aging adults’ functional ability. As people age, they experience impaired awareness, which affects their reasoning, cognitive ability, and consequently impedes their ability to manage or supervise ADLs successfully. Impaired awareness has a potential economic impact on aging adults. For example, recent studies have shown that aging adults with impaired awareness use more energy in homes due to deviation from normal execution of ADLs. This often results in incompletely executed ADLs which are left unattended or unsupervised. Unsupervised ADLs involving appliances with high wattage ratings (e.g., TV, air conditioner) can lead to an increase in the cost of home energy services used by aging adults who desire to age-in-place. Thus, in this paper, we propose a situation-aware framework that leverages a smart home resident's context information with respect to unattended ADLs in mitigating increased energy consumptions in smart home environments.
RRecT: Chinese Text Recognition with Radical-Enhanced Recognition Transformer

Text recognition has attracted continuous attention in recent years and reaches increasingly high performance on English datasets. Nevertheless, little attention is emphatically paid to Chinese Text Recognition (CTR), leading to a barely satisfying accuracy on Chinese datasets. Due to the complex glyph structure and large size of character set, CTR is more challenging and requires a powerful capacity of feature extraction. In this paper, we propose a novel network for CTR named Radical-enhanced Recognition Transformer (RRecT). It firstly introduces a customized Recognition Transformer (RecT) to extract multi-grained features, then exploits radical decomposition as an auxiliary supervision signal and enhances character representation with radical information by Radical Prediction Module (RPM) and Radical-Character Fusion Module (RCFM). Thus, final feature contains both character-level and fused radical-level information. The experimental results show that RRecT outperforms the state-of-the-art methods by a margin of 1.4% on Scene dataset, 1.8% on Document dataset and reaches a competitive performance on Web and Handwritten dataset. Moreover, RRecT requires much less computation cost and is a lightweight and effective model.
Generating Question-Answer Pairs for Few-Shot Learning

In the real world, obtaining question-answer pairs for a target domain text is often an expensive process, an approach to tackle the problem is to use automatically generated question-answer pairs from the problem context and large amount of unstructured texts (e.g. Wikipedia). However, current approaches to generate question-answer pairs typically require fine-tuning tens of thousands of examples to achieve good results. Obtaining these instances involves high labor costs, and once the model lacks sufficient training data, the performance of the model with few-shot (< 100 examples) drops dramatically. To address this problem, we propose a method for generating question-answer pairs with few examples, generating answers and questions using the input context, and then filtering the results through a model. By tuning the fine-tuned structure of the model to improve the few-shot performance, we also input different levels of features into the model through granularity decomposition, solving an important issue when data is limited: the inability to perform answer-span detection (or answer generation). We evaluate our method in three aspects: the diversity of generated answers, the quality of generated questions, and the training of a new model entirely using the generated QA pairs. Our experimental results demonstrate that our proposed method can effectively generate question-answer pairs with low resources.
Trans-Cycle: Unpaired Image-to-Image Translation Network by Transformer

Transformer recently has made remarkable progress in various computer vision tasks. In this paper, we design a novel transformer-based framework for image-to-image translation under the condition of limited computing resources. We conducted a comparative analysis between the model introduced in this paper and mainstream methodologies prevalent in the field, employing various publicly available datasets. The evaluation encompassed the assessment of numerical metrics and the examination of visualization outcomes. Compared with the recent unpaired translation network, our model has a more powerful deformation ability and a more refined distinction between image sub-regions. It has better performance on multiple datasets and much development potential.
S$$^2$$R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution

Nowadays, deep learning based methods have demonstrated impressive performance on ideal super-resolution (SR) datasets, but most of these methods incur dramatically performance drops when directly applied in real-world SR reconstruction tasks with unpredictable blur kernels. To tackle this issue, blind SR methods are proposed to improve the visual results on random blur kernels, which causes unsatisfactory reconstruction effects on ideal low-resolution images similarly. In this paper, we propose a double-win framework for ideal and blind SR task, named S $$^2$$ R, including a light-weight transformer-based SR model (S $$^2$$ R transformer) and a novel coarse-to-fine training strategy, which can achieve excellent visual results on both ideal and random fuzzy conditions. On algorithm level, S $$^2$$ R transformer smartly combines some efficient and light-weight blocks to enhance the representation ability of extracted features with relatively low number of parameters. For training strategy, a coarse-level learning process is firstly performed to improve the generalization of the network with the help of a large-scale external dataset, and then, a fast fine-tune process is developed to transfer the pre-trained model to real-world SR tasks by mining the internal features of the image. Experimental results show that the proposed S $$^2$$ R outperforms other single-image SR models in ideal SR condition with only 578K parameters. Meanwhile, it can achieve better visual results than regular blind SR models in blind fuzzy conditions with only 10 gradient updates, which improve convergence speed by 300 times, significantly accelerating the transfer-learning process in real-world situations. Codes will be found in https://github.com/berumotto-vermouth/S2R .
Rethinking CNN Architectures in Transformer Detectors

Since the introduction of Transformer into the field of object detection, numerous researchers have endeavored to leverage its strong long-distance dependency modeling capabilities. However, huge computational cost and lack of prior knowledge are always the pain points. In this paper, we try to find an alternative method to improve DETR-like models by rethinking the possibility of CNN in DETR from different perspectives. We propose a novel multi-scale patch embedding module, a new DETR encoder module and an auxiliary assessment strategy, which bring prior knowledge into DETR to accelerate the convergence, and enhance the final performance.
GenTC: Generative Transformer via Contrastive Learning for Receipt Information Extraction

Information Extraction from visually rich documents has attracted increasing attention due to its various advanced applications in the real world. Most existing methods employ sequence labeling models to solve this problem. However, these approaches suffer from error propagation problems, especially when dealing with noisy OCR results. For this reason, this paper proposes GenTC, a Generative Transformer enhanced by Contrastive learning for receipt information extraction. GenTC extracts structural information in a generative manner. In addition, since the optimization objective is inconsistent with the task, we use an entity-order perturbation and optimize the model with contrastive learning to mitigate the incorrect bias. GenTC is able to tolerate annotation errors in OCR results, which is vital because correct annotation of numerous documents is laborious and expensive. Extensive experiments on three public benchmark datasets demonstrate that GenTC achieves competitive performance compared with previous state-of-the-art methods, and outperforms them by a large margin, especially in realistic scenarios.
Evidential Robust Deep Learning for Noisy Text2text Question Classification

Text2text question classification (TQC) is a foundational task in the question classification (QC) field, with a wide range of applications in both industry and academia, such as intelligent customer service systems. Conventional QC tasks typically rely on one or more user-provided keywords to classify questions. In contrast, TQC problems involve categorizing semantically similar standard questions, which are then represented in short text format. However, due to the limited availability of TQC datasets, the process of manual labeling often results in noisy labels that do not accurately reflect the true class of a question, introducing bias into the training data. Noisy labels can lead to unreliable and uncertain supervised signals, which have a significant negative impact on the performance of models. To tackle these challenges, we propose the Evidential Robust Deep Learning (ERDL) framework, which integrates TQC Contrastive Loss (TCL) and TQC Evidential Learning Loss (TEL) to achieve accurate semantic similarity and handle noisy data in the TQC dataset. Notably, TEL is a novel loss function based on evidential learning that models the output as a Dirichlet distribution to capture the uncertainty resulting from noisy data. We evaluated our framework using four noisy TQC datasets and found that it outperformed relevant baselines, as indicated by the experimental results.