Reviewerly/exHarmony huggingface dataset:
Dative Ambiguity in Rusian: A Corpus Induced Study

Abstract When describing the Russian dative case, an observation often made in passing is that its assignment to certain types of arguments is ambiguous, particularly in constructions with a predicative infinitive. Thus far, no studies have put this problem into focus nor described the range of structures to which it applies. I approach this problem with corpus-driven methods. The present study shows that the predicate order and the referential prominence hierarchy can be used as explanatory variables in the modelling of the semanticsyntactic role assignment.
The Effect of (Historical) Language Variation on the East Slavic Lects Lematisers Performance

Abstract The need to develop tools for historical and regional variations is becoming more urgent in natural language processing. In this paper, we present two candidate systems for lemmatising historical East Slavic lects (Late Old East Slavic and Middle Russian), as well as modern regional East Slavic lects (Belogornoje and Megra): BERT-based end-to-end pipeline with language-specific heuristics and sequence-to-sequence BART-based encoderdecoder. To evaluate their predictions, we use accuracy score and string similarity measures, such as Levenshtein distance. The BERT-based model is more suitable for the regional data, achieving 85% accuracy score, and only 74% on the historical data. BART-based model climbs up to 92.6% accuracy score on the historical data, yet gets only 80% on the regional data. We provide an error analysis and discuss ways to enhance models, such as dictionary lookup and spellchecker.
Quadratic Polynomial Residual Network for No-Reference Image Quality Assessment

Residual connection has become an essential structure of deep neural networks. In residual connection, shallow features are directly superimposed to deep features without any processing. In this paper, a quadratic polynomial residual module is designed to increase the nonlinear fitting ability of the network. As the name suggests, this module superimposes quadratic polynomials of shallow features onto deep features. In this way, the series of two modules has the fitting ability of a quartic polynomial. The fitting ability of the network increases exponentially with the number of layers. According to Taylor’s theorem, it can be concluded that this module effectively improves the fitting ability of the network. Meanwhile, the image patches containing more information have greater contribution to image quality assessment. The patches are screened according to the two-dimensional information entropy, which reflects the information amount of patches. Based on the above two points, a quadratic polynomial residual network with entropy weighting and multi-receptive field structure is proposed for no-reference image quality assessment. The experimental results show that the proposed algorithm achieves high accuracy and more effectively fits the human visual system.
Interpreting Convolutional Layers in DNN Model Based on Time–Frequency Representation of Emotional Speech

Abstract The paper describes the relations of speech signal representation in the layers of the convolutional neural network. Using activation maps determined by the Grad-CAM algorithm, energy distribution in the time–frequency space and their relationship with prosodic properties of the considered emotional utterances have been analysed. After preliminary experiments with the expressive speech classification task, we have selected the CQT-96 time–frequency representation. Also, we have used a custom CNN architecture with three convolutional layers in the main experimental phase of the study. Based on the performed analysis, we show the relationship between activation levels and changes in the voiced parts of the fundamental frequency trajectories. As a result, the relationships between the individual activation maps, energy distribution, and fundamental frequency trajectories for six emotional states were described. The results show that the convolutional neural network in the learning process uses similar fragments from time–frequency representation, which are also related to the prosodic properties of emotional speech utterances. We also analysed the relations of the obtained activation maps with time-domain envelopes. It allowed observing the importance of the speech signals energy in classifying individual emotional states. Finally, we compared the energy distribution of the CQT representation in relation to the regions’ energy overlapping with masks of individual emotional states. In the result, we obtained information on the variability of energy distributions in the selected signal representation speech for particular emotions.
CapekDraCor: A New Contribution to the European Programable Drama Corpora

Abstract The aim of this paper is to present the new CapekDraCor corpus and the DraCor project with its research-oriented concept of a programmable corpora focused on quantitative analyses within the framework of computational literary studies. This digital platform extends the possibilities of large-scale drama analysis with a focus on the dramatic character(s). The basic operationalisation is the interaction within a dramatic configuration, i.e., the scenic co-presence of two speakers, from which network data are automatically extracted, both global networks of interactions of dramas and data characterising individual actors, i.e., literary characters. The paper demonstrates the CapekDraCor corpus, a new contribution to the extensive DraCor database, and presents the way the data are processed with respect to their specific multi-layered structure. The corpus contains all the plays written by Karel and Josef Čapek and the data are processed in a standardized format based on XML and general TEI guidelines for processing drama with a defined basic drama tagset. CapekDraCor also uses the newly created EZdrama format for data processing, which works as an intermediate step from .txt to .xml file as a lightweight YAML-like markup language. A file in this format can be automatically converted into a DraCor-ready XML file with a TEI header. The advantage of the programmable corpora concept is the possibility to use suitably structured data for drama research outside the DraCor platform and with other methods or tools for textual analysis. Simultaneously, this approach moves the researcher from the technical requirements of the analysis to operationalised computational analysis based on research questions and pre-prepared and flexible tools. DraCor is a unique open infrastructure (both in terms of data and tools) for the analysis of European drama, currently comprising 15 corpora in 10 different languages with a total of about 3,000 plays from a wide range of periods.
Metrics for Assessing Generalization of Deep Reinforcement Learning in Parameterized Environments

Abstract In this work, a study focusing on proposing generalization metrics for Deep Reinforcement Learning (DRL) algorithms was performed. The experiments were conducted in DeepMind Control (DMC) benchmark suite with parameterized environments. The performance of three DRL algorithms in selected ten tasks from the DMC suite has been analysed with existing generalization gap formalism and the proposed ratio and decibel metrics. The results were presented with the proposed methods: average transfer metric and plot for environment normal distribution. These efforts allowed to highlight major changes in the model’s performance and add more insights about making decisions regarding models’ requirements.
PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision

Although deep learning models have demonstrated impressive performance in various biomedical image segmentation tasks, their effectiveness heavily relies on a large amount of annotated training data, which can be costly to acquire. Semi-supervised learning (SSL) methods have emerged as a potential solution to mitigate this challenge by leveraging the abundance of unlabeled data. In this paper, we propose a highly effective SSL method for 3D biomedical image segmentation, called Pyramid Pseudo-Labeling Supervision (PPS). The PPS comprises three segmentation networks, forming a pyramid-like network structure. To enforce consistency in the outputs of the unlabeled data, we introduce a novel rectified pyramid consistency (RPC) loss. The PPS learns from the plentiful unlabeled data by minimizing the RPC loss, which ensures consistency between the pyramid predictions and the cycled pseudo-labeling knowledge among the three segmentation networks. Additionally, weak data augmentation is applied to perturb the inputs, further enhancing the consistency of the unlabeled data outputs. Experimental results demonstrate that our method achieves state-of-the-art performance on two publicly available 3D biomedical image datasets.
SAR Image Authentic Assessment with Bayesian Deep Learning and Counterfactual Explanations

Synthetic Aperture Radar (SAR) targets vary significantly with observation angles, which leads to the deep neural network being over-confident and unreliable under the condition of limited measurements. This issue can be addressed by means of data augmentation such as simulation or exemplar synthesis for unknown observations, however, the authenticity of simulated/synthesized data is critical. In this study, we propose a Bayesian convolutional neural network (BayesCNN) for SAR target recognition to attain better generalization and assess predictive uncertainty. Based on the measured uncertainty of BayesCNN’s prediction, we further propose to obtain the counterfactual explanation of an unrealistic SAR target generated by the unreliable simulation method. The experiments preliminarily demonstrates the proposed BayesCNN outperforms the counterpart frequentist neural network in terms of accuracy and confidence calibration when observation angles are constrained. In addition, the counterfactual explanation can reveal the non-authenticity of the augmented SAR target, which inspires us to filter the high-quality data, as well as to understand and improve the fidelity of generated SAR image in the future study.
Target Search Through Decentralized and Co-operative Surveillance of Multiple UAVs in an Uncertain Environment

Over the past few years, the use of Unmanned Aerial Vehicles (UAVs) has increased because of their greater adaptability and reduced mission cost. Recent developments in drone technology and the increased complexity of mission environment, have led to the use of multiple UAVs. Considering the dynamics of an uncertain environment, a Decentralized Cooperative Path Planning algorithm has been implemented in this paper which allows each agent to coordinate effectively in an area where communication with Ground Control Station is constrained. An Ant Colony Optimization based algorithm has been implemented for multiple UAVs to cooperatively search a given area. The developed algorithm incorporates manoeuvrability, collision avoidance, and threat avoidance constraints. An overall efficient path for each UAV is planned by using State Transformation Rule and Pheromone Update Mechanism at each time interval. An improved heuristic has been proposed as an improvement to the developed algorithm to reduce the manoeuvring cost of UAVs. Generated paths for each UAV have been validated by using Hardware in the Loop testing on multiple UAVs by using Dronekit-Python and Mission Planner. Results show that the use of multiple UAVs is effective for path planning in surveillance missions as it increases the coverage efficiency and reduces the overall mission time.
Corroborating Corpus Data with Elicited Introspection Data: A Case Study

Abstract The last decades have seen an exponential growth of corpus sizes. This development has been driven by a desire to investigate rare syntactic phenomena, but issues remain: Corpora are by definition finite samples, but language is by definition infinite, leading to the negative data problem (‘absence of evidence is not evidence of absence’). One solution is corroborating corpus data with elicited introspection data that is obtained in a reliable, valid, and objective way. I present a case study to show how this can be done using the Magnitude Estimation Test (MET) method (Hoffmann 2013). Analyzing elicited data from 37 L1 English speakers, I show that introspective data can complement corpus data and lead to interesting new findings.
Flow-Guided Diffusion Autoencoder for Unsupervised Video Anomaly Detection

Video anomaly detection (VAD) aims to automatically detect abnormalities that deviate from expected behaviors. Due to the heavy reliance of mainstream one-class methods on labeled normal samples, some unsupervised VAD methods have emerged. However, these methods are unable to detect both appearance and motion anomalies in videos comprehensively. To address the above problem, we present for the first time a Flow-guided Diffusion AutoEncoder (FDAE) that generates objects of each frame to detect anomalous in an unsupervised manner. Our model takes foreground objects and motion information as inputs to train a conditional diffusion autoencoder for foreground reconstruction. To make our model concentrate on learning normal samples, we further design a sample refinement scheme and introduce a mixed Gaussian clustering network to enhance the capability of the diffusion model in capturing typical characteristics of normal samples. Comprehensive experiments on three public available datasets demonstrate that the proposed FDAE outperforms all competing unsupervised approaches.
The Epistemic Marker <i>Určit Ě</i> in the Light of Corpus Data

Abstract The paper presents a pilot study for a research project on epistemic modality and/or evidentiality markers in Czech. The study focuses on the expression určitě. Although, this marker is typically considered to signal high certainty, the dictionary of standard Czech (Slovník spisovné češtiny, SSČ) also offers an alternative meaning of probability , indicating a lower degree of certainty. We use parallel data from the InterCorp v15 corpus to determine whether the probability meaning can be identified unequivocally in real language data and whether it correlates with specific translation equivalents, linguistic features, or lexical context. Based on our findings, we propose an alternative method for distinguishing between different shades of meaning based on the communicative functions of the utterances, and we draw conclusions regarding the relevance of individual grammatical and lexical clues in context for future annotations.
Annotation of Analytic Verb Forms in Czech – Complex Cases

Abstract The article deals with complex cases of determining the attribute verbtag, which contains the values of morphosyntactic categories of analytic verb forms. The latest corpora of contemporary written Czech from the SYN series are tagged with this attribute. In this paper, we focus on cases where it is difficult to identify values of verbtag categories. These include, e.g. the identification of the auxiliary verb být ‘to be’, recognition of the mood and tense of coordinated participles, or determining the number in compound forms in which the individual parts have a different morphological number. Some of the problems are of a theoretical nature, since it is not clear what the correct solution should be. Here we have arbitrarily opted for one option that was offered. Other problems are due to imperfections in the algorithms we use for annotation. The solution here is to improve these algorithms.
Text Vectorization Techniques Based on Wordnet

Abstract The utilization of text vectorization techniques has become essential for numerous classification tasks in present-day natural language processing. Word embedding methods commonly used today, such as Word2Vec, GloVe, etc., are based on the semantic similarity of words. WordNet, as a lexical database of words, provides a rich source of semantic information. In our article, we propose a text vectorization technique using extended text data with the data augmentation method, specifically by replacing words with their synonyms obtained from WordNet. The results obtained from text classification tasks using multiple classifiers demonstrate that expanding the corpus with this method leads to improved vector representations of words.
A High-Resolution Network Based on Feature Redundancy Reduction and Attention Mechanism

Model lightweighting is an essential aspect of computer vision tasks. We found that HRNet achieves superior performance by maintaining high resolution through multiple parallel subnetworks, but it also introduces many unnecessary redundant features, resulting in high complexity. Many methods replace modules in the backbone network with lightweight ones, often based on MobileNet, Sandglass modules, or ShuffleNet. However, these methods often suffer from significant performance degradation. This paper proposes GA-HRNet, a lightweight high-resolution human pose estimation network with an integrated attention mechanism, built upon the HRNet framework. The network structure of HRNet is restructured with reference to the G-Ghost Stage, introducing GPU-efficient cross-layer cheap operations to reduce inter-block feature redundancy, significantly lowering complexity while preserving high accuracy. Moreover, to compensate for the accuracy loss due to reconstruction, an attention module is designed and introduced to emphasize more important information in the channel and spatial dimensions, thereby improving network precision. Experimental results on the COCO and COCO-WholeBody datasets demonstrate that the proposed GA-HRNet is lighter and yet more accurate than HRNet. Moreover, it outperforms several state-of-the-art methods in terms of performance.
Through Derivational Relations to Valency of Nonverbal Predicates in the Nomvallex Lexicon

Abstract NomVallex is a manually annotated valency lexicon of Czech nouns and adjectives that enables a comparison of valency properties of derivationally related lexical units. We present new developments in how the lexicon facilitates research into changes in valency across part-of-speech categories and derivational types. In particular, it provides links from derived lexical units to their base lexical units and also allows to search and display a base lexical unit together with all lexical units directly derived from it. Using an automatic procedure, any difference in valency between two derivationally related lexical units is specified. As a case study, focusing on nouns and adjectives directly or indirectly motivated by verbs, the facilities provided by the lexicon are used to show differences in what ways the particular deverbal derivatives representing various derivational types express the valency complementation standing in the base verbal construction in the subject position.
PAT-Unet: Paired Attention Transformer for Efficient and Accurate Segmentation of 3D Medical Images

Due to the remarkable performance of Transformers in 2D medical image segmentation, recent studies have incorporated them into 3D medical segmentation tasks. Compared to convolution operations in CNNs, Transformer-based models possess self-attention, allowing them to capture long-range dependencies among pixels. To address the high computational cost of the Transformer architecture when dealing with volumetric images containing a large number of slices, we propose an efficient hybrid CNN-Transformer architecture for 3D medical image segmentation named PAT-Unet. Firstly, our proposed Paired Attention Transformer (PAT) blocks effectively reduce spatial dimensions while proficiently learning channel and spatial information in 3D feature maps. This leads to improved segmentation performance by reducing parameter count and accelerating computation speed. Secondly, our Deformable Enhanced Skip Connection (DESC) module captures detailed features in irregular lesion areas by learning volume spatial offsets. Finally, we experimentally validate the effectiveness and efficiency of our model on the Synapse and ACDC benchmark datasets. On the Synapse dataset, our model achieves a Dice similarity score of 87.17%, reducing parameters and FLOPs by 67% compared to the best existing methods reported in the literature.
Quality-Aware CLIP for Blind Image Quality Assessment

Blind Image Quality Assessment (BIQA) aims to simulate human perception of image quality without reference images. Pretrained visual-linguistic models, like CLIP, have shown excellent performance in various visual tasks and have been successfully applied in BIQA. However, existing CLIP-based approaches typically employ a coarse classification method, dividing images into two or five quality levels based on CLIP’s text-image comparison ability. In this work, we propose a novel approach for BIQA that introduces a fine-grained quality-level stratification strategy. This strategy enables a more precise assessment of image quality across a wider range of levels. Additionally, we present a two-stage training model called Quality-Aware CLIP (QA-CLIP). In the first stage, we leverage a set of learnable text tokens to optimize the text description and fully utilize the representation capabilities of CLIP’s text encoder. In the second stage, we further optimize the image encoder and quality-aware block to capture features that are highly relevant to perceived quality. Experimental results demonstrate that QA-CLIP achieves comparable performance with state-of-the-art methods on various synthetic and real datasets. Notably, in CSIQ, TID2013, and KADID datasets, QA-CLIP outperforms the state-of-the-art by 1.2%, 4.7%, and 4.8% respectively in terms of Spearman Rank Correlation Coefficient (SRCC).
Multi-modality Fusion Based Lung Cancer Survival Analysis with Self-supervised Whole Slide Image Representation Learning

Whole slide pathological images (WSIs) are the gold standard for lung cancer prognosis. However, due to their high resolution and limited annotations, lung cancer survival analysis based on WSIs becomes a challenging task. Some recent methods that fuse WSI and other modalities have achieved certain results. However, these methods tend to focus on integrating gene related information while overlooking relatively easily obtainable clinical variables and often rely on labor-intensive ROI annotations. In this work, we propose a novel framework for lung cancer survival analysis, which obviates the need for ROI annotations and fully exploits WSIs and clinical information by introducing a multi-modality fusion module and multi-task learning. We also utilizes self-supervised learning to eliminate the heterogeneity between WSIs and natural images. Experimental results via 5-fold cross-validation on 1,225 WSIs from 444 patients from NLST validate the state-of-the-art performance of our proposed method.
A Shape-Based Quadrangle Detector for Aerial Images

The performance of oriented object detectors has been adversely impacted by the substantial variations in object orientation. In this paper, we propose a simple but efficient object detection framework for oriented objects in aerial images, termed QuadDet. Instead of adopting oriented bounding box to represent the object, we directly predict the four vertices of the object’s quadrilateral. Specially, we introduce a fast sorting method for four vertexes of quadrangles, called the Vertex Sorting Function. The function confirms that the vertexes can compose a valid quadrangle by sorting tangents of the vertexes. Furthermore, we employ an efficient polygon IoU loss function, named the PolyIoU Loss Function, to progressively align the predicted quadrangle’s shape with the ground truth. Under these strategies, our model achieves competitive performance. Without bells and whistles, our method with ResNet50 achieves 73.63% mAP on the DOTA-v1.0 dataset running at 23.4 FPS, which surpasses all recent one-stage oriented object detectors by a significant margin. Moreover, on the largest dataset DOTA-v2.0, our QuadDet with ResNet50 obtains 51.54% mAP. The code and models are available at https://github.com/DDGRCF/QuadDet .