Reviewerly/exHarmony huggingface dataset:
HTC-Net: A hybrid CNN-transformer framework for medical image segmentation

Automated medical image segmentation is a crucial step in clinical analysis and diagnosis, as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks (DCNNs) have been widely used in the medical field, achieving excellent results. The high complexity of medical images poses a significant challenge for many networks in balancing local and global information, resulting in unstable segmentation outcomes. To address the challenge, we designed a hybrid CNN-Transformer network to capture both the local and global information. More specifically, deep convolutional neural networks are introduced to exploit the local information. At the same time, we designed a trident multi-layer fusion (TMF) block for the Transformer to fuse contextual information from higher-level (global) features dynamically. Moreover, considering the inherent characteristic of medical image segmentation (e.g., irregular shapes and discontinuous boundaries), we developed united attention (UA) blocks to focus on important feature learning. To evaluate the effectiveness of our proposed approach, we performed experiments on two publicly available datasets, ISIC-2017, and Kvasir-SEG, and compared our results with state-of-the-art approaches. The experimental results demonstrate the superior performance of our approach. The codes are available at https://github.com/Tanghui2000/HTC-Net.
How anti-gender and gendered imagery translate the Great Replacement conspiracy theory in online far-right platforms

This article is based on a case study of online media practices of the Czech far-right group Angry Mothers, the biggest far-right Facebook group in the Czech political context in 2018. We show how the group used visual storytelling to translate the narratives of the Great Replacement conspiracy theory, which has become one of the most dominant narratives in European and US far-right discourses over recent years. The conspiracy theory was introduced in the book Le Grande Replacement by Renaud Camus in 2011 and claims that powerful Jewish elites use their financial resources to promote ‘Lesbian, gay, bisexual, transgender, queer, intersex, asexual + (LGBTQIA+) and gender ideologies’ and multiculturalism to destroy the white race, which will eventually die out. Based on theories of translation and visual storytelling, we demonstrate how the main tropes of its anti-Semitic narrative were diffused through the use of images stigmatising LGBTQIA+ people and other minorities in online communications of the Czech far-right group Angry Mothers.
Linear codes of larger lengths with Galois hulls of arbitrary dimensions and related entanglement-assisted quantum error-correcting codes

Galois hulls are generalizations of Euclidean and Hermitian hulls, which have attracted interest because of their important applications in determining the complexity of some algorithms about linear codes and in constructing entanglement-assisted quantum error-correcting codes (EAQECCs). In this paper, the whole contribution is two-folded. On one hand, we propose explicit methods to construct linear codes of larger lengths with Galois hulls of arbitrary dimensions from given Galois self-orthogonal codes. Conditions required in our approach are proven to be relatively weak. On the other hand, we apply these results to construct EAQECCs. Two bounds for EAQECCs constructed from linear codes with prescribed dimensional Galois hull are given and EAQECCs with rates greater than or equal to 12 and positive net rates can be obtained. We also present many interesting examples to explain visually how these two aspects work.
Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization

Visualizing spatial correlations in 3D ensembles is challenging due to the vast amounts of information that need to be conveyed. Memory and time constraints make it unfeasible to pre-compute and store the correlations between all pairs of domain points. We propose the embedding of adaptive correlation sampling into chord diagrams with hierarchical edge bundling to alleviate these constraints. Entities representing spatial regions are arranged along the circular chord layout via a space-filling curve, and Bayesian optimal sampling is used to efficiently estimate the maximum occurring correlation between any two points from different regions. Hierarchical edge bundling reduces visual clutter and emphasizes the major correlation structures. By selecting an edge, the user triggers a focus diagram in which only the two regions connected via this edge are refined and arranged in a specific way in a second chord layout. For visualizing correlations between two different variables, which are not symmetric anymore, we switch to showing a full correlation matrix. This avoids drawing the same edges twice with different correlation values. We introduce GPU implementations of both linear and non-linear correlation measures to further reduce the time that is required to generate the context and focus views, and to even enable the analysis of correlations in a 1000-member ensemble.
PoSE: Suppressing Perceptual Noise in Embodied Agents for Enhanced Semantic Navigation

Embodied agents navigating unknown environments face the challenge of optimizing exploration based on semantic information. Conventional methods, reliant on collected data or pre-defined rules, have limitations in scalability and applicability, while pretrained language models based methods focusing on textual modality encounter perceptual noise, which affects decision-making. To mitigate these problems, this paper presents Prompt-based Vision Context Semantic Exploration (PoSE), an innovative method that leverages prior knowledge from vision language models (VLMs) to suppress perceptual noise. Through its unique design of prompts that use existential logic, PoSE reduces misidentifications of target objects within the observed environment. It also introduces a unique exploration map to translate target locations into exploration coordinates. The proposed methodology is evaluated on the ALFRED benchmark, demonstrating improved performance against previous rulebased and task-specific data-driven exploration policies. Furthermore, PoSE's semantic exploration policy offers enhancements over pretrained language-model based exploration methods that focus on text modality, showcasing its effectiveness and generality.
Heterogeneous Graph Contrastive Learning With Metapath-Based Augmentations

Heterogeneous graph contrastive learning is an effective method to learn discriminative representations of nodes in heterogeneous graph when the labels are absent. To utilize metapath in contrastive learning process, previous methods always construct multiple metapath-based graphs from the original graph with metapaths, then perform data augmentation and contrastive learning on each graph respectively. However, this paradigm suffers from three defects: 1) It does not consider the augmentation scheme on the whole metapath-based graph set, which hinders them from fully leveraging the information of metapath-based graphs to achieve better performance. 2) The final node embeddings are not optimized from the contrastive objective directly, so they are not guaranteed to be distinctive enough. It leads to suboptimal performance on downstream tasks. 3) Its computational complexity for contrastive objective is high. To tackle these defects, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> eterogeneous <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontrastive learning model with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> etapath-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ugmentations ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HGCMA</b> ), which is designed for downstream tasks with a small amount of labeled data. To address the first defect, both semantic-level and node-level augmentation schemes are proposed in our HGCMA for augmentation, where a metapath-based graph and a certain ratio of edges in each metapath-based graph are randomly masked, respectively. To address the second and third defects, we utilize a two-stage attention aggregation graph encoder to output final node embedding and optimize them with contrastive objective directly. Extensive experiments on three public datasets validate the effectiveness of HGCMA when compared with state-of-the-art methods.
Alternatives for the q-matroid axioms of independent spaces, bases, and spanning spaces

It is well known that in q-matroids, axioms for independent spaces, bases, and spanning spaces differ from the classical case of matroids, since the straightforward q-analogue of the classical axioms does not give a q-matroid. For this reason, a fourth axiom has been proposed. In this paper we show how we can describe these spaces with only three axioms, providing two alternative ways to do that. As an application, we show direct cryptomorphisms between independent spaces and circuits and between independent spaces and bases.
EsArCost: Estimating repair costs of software architecture erosion using slice technology

Software architecture erosion has a negative effect on software quality, software performance and evolution cost, so repairing architecture erosion is an important task. However, in the actual evolution process, due to the pressure of cost, it is not practical to repair all erosion problems. To repair more erosion problems at a certain total cost, developers would better know the appropriate repair cost of each erosion problem. In this paper, we propose an approach called EsArCost, which can locate the reasons for architecture erosion and estimate the repair cost of each erosion problem. To this end, EsArCost detects multi-level changes of software architecture and calculates the architecture erosion degree, then locates which changed codes cause architecture erosion. EsArCost further calculates the full slice of each erosion problem to estimate the difficulty and repair costs. We evaluate our approach on small and large open source programs, and the experiment results indicate that EsArCost can effectively and efficiently estimate repair costs.
Underwater image enhancement method based on golden jackal optimization

The propagation of light underwater is affected by water absorption and scattering, resulting in poor visibility of obtained underwater images, and exhibiting degradation phenomena such as color deviation and low contrast. To improve the quality and visibility of underwater images, a method of underwater image enhancement based on Golden Jackal optimization is proposed. The algorithm consists of two processing stages. The first stage is mainly processed in the RGB color space. Based on the attenuation characteristics of underwater color channels, a constructed color compensation and correction strategy is used to eliminate color deviation caused by water absorption. In second stage, it is mainly processed in the Lab color space, using the Golden Jackal optimization algorithm and fitness function to search the optimal parameters of transformation function to improve the image contrast by optimizing the grey intensity of L component and its histogram distribution. The feasibility and effectiveness of the proposed method have been verified through testing and analysis of much underwater degraded image data. In subjective and objective analysis with multiple state-of-the-art underwater image clarity methods, the proposed method performs excellent in color restoration and detail texture enhancement. Further experiments have shown that the proposed method has strong robustness and universality. It is not only suitable for enhancing various underwater scene images, but also for enhancing environmental images with fog, low lighting, and sandstorms. Meanwhile, the proposed method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection.
Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments

Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.
MFI-YOLO: Multi-fault insulator detection based on an improved YOLOv8

Insulators are essential components in power transmission lines. Due to the harsh variations in bad environments, insulators may experience faults. Detecting these insulator faults promptly and effectively is an urgent issue. To rapidly and accurately locate insulators and their faulty regions in aerial images of insulators with complex backgrounds and varying fault sizes, this paper proposes an improved YOLOv8 algorithm for the detection of multiple insulator fault types (MFI-YOLO). This algorithm achieved target feature extraction in complex background images by replacing the C2F network constructed by fusing the GhostNet and multi-scale asymmetric convolution (MSA-GhostBlock). Furthermore, in the feature fusion stage, a multi-scale feature fusion structure called ResPANet, based on residual skip connections, was constructed to replace the PANet. This enhancement aims to improve the network detection accuracy in multi-target scenarios. Finally, to evaluate the algorithm's performance, this study constructed a target detection dataset containing four types of insulators: normal, self-explosive, damaged, and flashover. Experimental results indicate that, compared to the original model, the improved model has increased mean accuracy from 89.2% to 93.9%. The designed model exhibits high detection accuracy in the insulator and its three fault categories, especially for some hard-to-detect fitting.
Reading compound words in Finnish and Chinese: An eye-tracking study

Two eye-tracking experiments in alphabetic Finnish and two in logographic Chinese examined the recognition of two-constituent compound words in reading. In Finnish, two-constituent compound words vary greatly in length, whereas in Chinese they are identical in length. According to the visual acuity principle (Bertram & Hyönä, 2003), short Finnish compound words and all two-character Chinese compound words that fit in foveal vision are recognized holistically, whereas long Finnish compound words are recognized via components. Experiment 1 in Finnish provided evidence consistent with the account, whereas the results for long compound words presented in condensed font in Experiment 2 were inconsistent with it. In Chinese, the first-character frequency effect was non-significant even when the compound words were presented in large font. The Finnish results suggest that componential processing is necessary when the compound word entails more than 10 letters. The Chinese results are compatible with the Chinese Reading Model (Li & Pollatsek, 2020) that assumes whole-word representations to overrule the activation of components during compound word recognition.
Deep learning-based software bug classification

Accurate classification of bugs can help accelerate the bug triage process, code inspection, and repair activities. In this context, many machine learning techniques have been proposed to classify bugs. The expressive power of deep learning could be used to further improve classification. We propose a novel deep learning-based bug classification approach. We first build a bug taxonomy with eight bug classes, each characterized by a set of keywords. Subsequently, we heuristically annotate a moderately large set (∼1.36M) of software bug resolution reports using an earth-mover distance technique based on the keywords. Finally, we use four attention-based classification techniques to classify these curated bugs. Our experiments on a carefully collected dataset indicate that our proposed technique achieved a mean F1-Score of 84.78% and a mean macro-average ROC of 98.25%. Our proposed approach was observed to outperform the existing techniques by 16.88% on an average in terms of F1-Score for the considered dataset.
Complex amplitude extraction from a set of interferograms by simultaneous modulation of amplitude and phase steps

The measurement of an optical field arises in various fields of science and engineering, including holography, crystallography, microscopy, and many others. Nowadays, there are several methods to estimate amplitude and phase including Shack-Hartman wavefront sensor, algorithms type Gerchberg-Saxton, algorithm-based on the transport of irradiance equation, holographic, interferometric, among others. This paper introduces an interferometric method for retrieving the complex amplitude from a set of interferograms modulated simultaneously in visibility and phase steps. Instead of phase only as the method of so-called phase-shifting interferometry. This approach is based on the modulation of amplitude and phase steps and is presented for the case of synchronous detection and coherence light. It is validated with a numerical noise study, turning out to be accurate and robust to detuning errors, with low distortion, and a very high signal-to-noise. The experiment is carried out on a Mach–Zehnder interferometer adapted to modulate the reference amplitude, and the phase steps by modulation of polarization, instead of phase steps only as in phase-shifting interferometry.
Unawareness detection: Discovering black-box malicious models and quantifying privacy leakage risks

Because machine learning models, especially black-box malicious models vulnerable to attribute inference attacks, are capable of generating a great deal of privacy leakage, recent work has focused on assessing these models in an attempt to prevent unexpected attribute privacy leakage. While there has been some success at model privacy risk evaluations, these traditional solutions are almost brittle in practice because they not only require white-box access to obtain model feature layer outputs but also their evaluation results are heavily influenced by the training dataset and the model structure, leading to difficulty in generalization. In this paper, we propose a novel unawareness detection mechanism for discovering black-box malicious models and quantifying potential unawareness privacy leakage risk along with machine learning models to overcome the two limitations. A new method for quantifying the privacy risk caused by a specific loss function has been proposed to mitigate the impact of the training dataset and the model structure. A new evaluation model has also been proposed that uses Matthew's correlation coefficient score as a new metric and the final output of the target model as a new input. In addition, the theoretical upper bound of the model privacy risk has also been given a mathematical formula that is positively correlated to the mutual information between the sensitive attributes and the target model outputs. Compared with traditional detection methods, our evaluation model reduces the requirement for model access and minimizes evaluation errors caused by data imbalance, and our privacy risk assessment method and theoretical upper bounds on privacy risk can be applied to a broader range of datasets and target model structures. The experimental results show that the adversary's prediction capability is affected by the distribution of datasets and the level of malicious intent of the model, which is consistent with the theoretical prediction, and the detecting method can find potential model privacy leaks in public datasets UTKFace and FairFace.
Numerical model study on influences of photovoltaic plants on local microclimate

The construction of large-scale photovoltaic plants will inevitably change the local environment. We consider that the influences of such increasing large-scale photovoltaic installations on radiation, temperature, wind profile and other factors within the region can be described in a layer of photovoltaic canopy. This paper improves the UCRC-Solar model for photovoltaic plants, with reference to the calculation of sensible heat flux of the underlying forest surface. Model verification was done with measured data from the Red Rock photovoltaic plant, Arizona, USA. In this study, it indicates that photovoltaic plant has locally generated heat island effects in daytime. The calculation of local microclimate changes before and after the construction of the photovoltaic plants and, the impact of photovoltaic coverage finds an increase in the temperature daily range and a reduction in the ground temperature by about 5 °C. Furthermore, a 15 % increase in photovoltaic coverage increased the daily 3 m air temperature difference by 0.55 °C and reduced the ground temperature by 3.6 °C in the day and 1.1 °C at night, respectively. In the future, rain and snow processes would be considered to further explore the microclimate impact of photovoltaic plants.
Real-time Multi-CNN based Emotion Recognition System for Evaluating Museum Visitors’ Satisfaction

Conventional studies on the satisfaction of museum visitors focus on collecting information through surveys to provide a one-way service to visitors, and thus it is impossible to obtain feedback on the real-time satisfaction of visitors who are experiencing the museum exhibition program. In addition, museum practitioners lack research on automated ways to evaluate a produced content program's lifecycle and its appropriateness. To overcome these problems, we propose a novel multi-convolutional neural network, called VimoNet, which is able to recognize visitors emotions automatically in real-time based on their facial expressions and body gestures. Furthermore, we design a user preference model of content and a framework to obtain feedback on content improvement for providing personalized digital cultural heritage content to visitors. Specifically, we define seven emotions of visitors and build a dataset of visitor facial expressions and gestures with respect to the emotions. Using the dataset, we proceed with feature fusion of face and gesture images trained on the DenseNet-201 and VGG-16 models for generating a combined emotion recognition model. From the results of the experiment, VimoNet achieved a classification accuracy of 84.10%, providing 7.60% and 14.31% improvement, respectively, over a single face and body gesture-based method of emotion classification performance. It is thus possible to automatically capture the emotions of museum visitors via VimoNet, and we confirm its feasibility through a case study with respect to digital content of cultural heritage.