Initial Features Research Articles

Graph-Based Progressive Fusion Network for Multi-Modality Vehicle Re-Identification

Vehicle re-identification (Re-ID) is a critical task in intelligent transportation, aiming to match vehicle images of the same identity captured by non-overlapping cameras. However, it is difficult to achieve satisfactory results based on RGB images alone in darkness. Therefore, it is of great importance to consider multi-modality vehicle re-identification. Currently, the proposed works deal with different modality features through direct summation and fusion based on heat map, which however ignores the relationship between them. Meanwhile, there is a huge gap between the different modalities, which needs to be reduced. In this paper, to solve the above two problems, we propose a Graph-based Progressive Fusion Network (GPFNet) using a graph convolutional network to adaptively fuse multi-modality features in an end-to-end learning framework. GPFNet consists of a CNN feature extraction module (FEM), a GCN feature fusion module (FFM), and a loss function module (LFM). Firstly, in FEM, we employ a multi-stream network architecture to extract single-modality features and common-modality features and employ a random modality substitution module to extract mixed-modality features. Secondly, in FFM, we design an efficient graph structure to associate the features of different modalities and adopt a progressive two-stage strategy to fuse them. Finally, in LFM, we use GCN-aware multi-modality loss to constrain the features. For reducing modality differences and contributing better initial mixed-modality features to FFM, we propose random modality substitution as a data enhancement method for multi-modality datasets. Extensive experiments on multi-modality vehicle Re-ID datasets RGBN300 and RGBNT100 show that our model achieves state-of-the-art performance.

CT-based dosiomics and radiomics model predicts radiation-induced lymphopenia in nasopharyngeal carcinoma patients.

This study aims to develop and validate a model predictive for the incidence of grade 4 radiation-induced lymphopenia (G4RIL), based on dosiomics features and radiomics features from the planning CT of nasopharyngeal carcinoma (NPC) treated by radiation therapy. The dataset of 125 NPC patients treated with radiotherapy from August 2018 to March 2019 was randomly divided into two sets-an 85-sample training set and a 40-sample test set. Dosiomics features and radiomics features of the CT image within the skull bone and cervical vertebrae were extracted. A feature selection process of multiple steps was employed to identify the features that most accurately forecast the data and eliminate superfluous or insignificant ones. A support vector machine learning classifier with correction for imbalanced data was trained on the patient dataset for prediction of RIL (positive classifier for G4RIL, negative otherwise). The model's predictive capability was gauged by gauging its sensitivity (the likelihood of a positive test being administered to patients with G4RIL) and specificity in the test set. The area beneath the ROC curve (AUC) was utilized to explore the association of characteristics with the occurrence of G4RIL. Three clinical features, three dosiomics features, and three radiomics features exhibited significant correlations with G4RIL. Those features were then used for model construction. The combination model, based on nine robust features, yielded the most impressive results with an ACC value of 0.88 in the test set, while the dosiomics model, with three dosiomics features, had an ACC value of 0.82, the radiomics model, with three radiomics features, had an ACC value of 0.82, and the clinical model, with its initial features, had an ACC value of 0.6 for prediction performance. The findings show that radiomics and dosiomics features are correlated with the G4RIL of NPC patients. The model incorporating radiomics features and dosiomics features from planning CT can predict the incidence of G4RIL in NPC patients.

A Conceptual Design of an AI-Enabled Decision Support System for Analysing Donor Behaviour in Nonprofit Organisations

Analysing and understanding donor behaviour in nonprofit organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an artificial-intelligence-enabled decision-support system (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a design science research approach to evaluate an AI-enabled DSS’s initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment by conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The evaluation of the conceptual design results led to the recommendation for efficiency, effectiveness, flexibility, and usability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSSs for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such an analysis of donors who donate money or volunteer time for NPOs.

Fast grading method based on data driven capacity prediction for high-efficient lithium-ion battery manufacturing

With the large-scale expansion of the battery market, the cost optimization of battery manufacturing has become a focus of attention. Among the complex production process of the battery, capacity grading requires a full discharge to measure the capacity and results in high cost. This study proposes a fast grading method in which the batteries are half discharged and graded according to the capacity predicted by a neural network. The prediction-based method takes half the time and saves about 37 % of the energy consumption. Twenty-three features are extracted as the initial features. The collinear features are screened-out, and three feature reduction methods are compared. Permutation importance can effectively clarify the nonlinear relationship and determine the critical features. The root-mean-square error of the testing set is 0.18 %. The method is feasible and has the potential for further evolution. The electrochemical mechanism involved in the features is analyzed by simulation. Under the guidance of the mechanism, the method can be transferred to other batteries.

A Novel CT-Based Radiomic Analysis for the Prediction of Overall Survival in Rectal Cancer Patients Treated with Short Course RT Total Neoadjuvant Therapy

Radiomics is a method based on the extraction of features from medical images, enriching the construction of predictive models for aiding patient management. The purpose of this study is to develop a model based on radiomic features for the overall survival (OS) in rectal cancer patients treated with total neoadjuvant therapy (TNT). Prediction of neoadjuvant rectal (NAR) score was also preliminarily evaluated. A retrospective cohort including patients treated with TNT at a single center between 2017-2021 was identified. TNT consisted of short course pelvic radiotherapy (25 Gy in 5 fractions) followed by FOLFOX or XELOX. A single CTV included mesorectum and nodal regions. Two endpoints were used: OS at 3 years (from the start of RT) and NAR score binary classification. A cut-off of 8.4 was used for NAR based on a previous local study. CTV radiomic features were extracted from simulation CTs using Pyradiomics v3. Features were extracted from the unfiltered images as well as from a set of eight filtered images. Radiomic features consist of 7 classes: shape, first order statistics, glcm, gldm, glrlm, glszm and ngtdm. 1967 initial features were extracted. Features with 0 standard deviation were removed as well as highly correlated ones (0.99 Pearson Coefficient). Dimension reduction using PCA was applied to each feature class, preserving 95% of the explained variance. Supervised algorithms were evaluated using a 10-fold cross-validation technique. Demographic and clinical features were also considered (age, sex, cT stage and N positivity) RESULTS: Eighty-two patients were included (37 females). Median age was 61.5 years (IQR 53-70). Clinical staging was comprised of 5 cT2 patients, 57 cT3 and 20 cT4, with 69 cN+. 3y-OS was 72%, with a median follow-up of 43.7 months (IQR 35.3-48.6). For the 3y-OS endpoint two prediction models were developed. First, a reference model (KNN classifying algorithm) based on demographic and clinical features only resulting in an AUC of 0.659 (SD 0.029). The second model (CART classifying algorithm) included demographic, clinical and radiomic features resulting in an AUC of 0.744 (SD 0.032). For the binary NAR classification model, a subset of 77 patients was used. Twenty-three presented a NAR<8.4 and 54 a NAR≥8.4. The CART algorithm presented the best performance with an AUC of 0.808 (SD 0.037) considering demographic, clinical and radiomic features. Radiomic models were developed for 3y-OS prediction and binary NAR classification. The addition of radiomic features to clinical and demographic information improved the predictive capability of the 3y-OS model. Radiomic prediction of NAR makes it a promising tool for the identification of potential short course RT TNT responders.

FILNet: Fast Image-Based Indoor Localization Using an Anchor Control Network.

This paper designs a fast image-based indoor localization method based on an anchor control network (FILNet) to improve localization accuracy and shorten the duration of feature matching. Particularly, two stages are developed for the proposed algorithm. The offline stage is to construct an anchor feature fingerprint database based on the concept of an anchor control network. This introduces detailed surveys to infer anchor features according to the information of control anchors using the visual-inertial odometry (VIO) based on Google ARcore. In addition, an affine invariance enhancement algorithm based on feature multi-angle screening and supplementation is developed to solve the image perspective transformation problem and complete the feature fingerprint database construction. In the online stage, a fast spatial indexing approach is adopted to improve the feature matching speed by searching for active anchors and matching only anchor features around the active anchors. Further, to improve the correct matching rate, a homography matrix filter model is used to verify the correctness of feature matching, and the correct matching points are selected. Extensive experiments in real-world scenarios are performed to evaluate the proposed FILNet. The experimental results show that in terms of affine invariance, compared with the initial local features, FILNet significantly improves the recall of feature matching from 26% to 57% when the angular deviation is less than 60 degrees. In the image feature matching stage, compared with the initial K-D tree algorithm, FILNet significantly improves the efficiency of feature matching, and the average time of the test image dataset is reduced from 30.3 ms to 12.7 ms. In terms of localization accuracy, compared with the benchmark method based on image localization, FILNet significantly improves the localization accuracy, and the percentage of images with a localization error of less than 0.1m increases from 31.61% to 55.89%.

The Pathogenesis of Systemic Sclerosis: The Origin of Fibrosis and Interlink with Vasculopathy and Autoimmunity.

Systemic sclerosis (SSc) is an autoimmune disease associated with increased mortality and poor morbidity, impairing the quality of life in patients. Whilst we know that SSc affects multiple organs via vasculopathy, inflammation, and fibrosis, its exact pathophysiology remains elusive. Microvascular injury and vasculopathy are the initial pathological features of the disease. Clinically, the vasculopathy in SSc is manifested as Raynaud's phenomenon (reversible vasospasm in reaction to the cold or emotional stress) and digital ulcers due to ischemic injury. There are several reports that medications for vasculopathy, such as bosentan and soluble guanylate cyclase (sGC) modulators, improve not only vasculopathy but also dermal fibrosis, suggesting that vasculopathy is important in SSc. Although vasculopathy is an important initial step of the pathogenesis for SSc, it is still unclear how vasculopathy is related to inflammation and fibrosis. In this review, we focused on the clinical evidence for vasculopathy, the major cellular players for the pathogenesis, including pericytes, adipocytes, endothelial cells (ECs), and myofibroblasts, and their signaling pathway to elucidate the relationship among vasculopathy, inflammation, and fibrosis in SSc.

More severe initial manifestations and worse short-term functional outcome of intracerebral hemorrhage in the plateau than in the plain

Intracerebral hemorrhage (ICH) is one of the most devastating forms of stroke. However, studies on ICH at high altitude are insufficient. We aimed to compare the initial manifestations, imaging features and short-term functional outcomes of ICH at different altitudes, and further explore the effect of altitude on the severity and prognosis of ICH. We retrospectively recruited ICH patients from January 2018 to July 2021 from two centers at different altitudes in China. Information regarding to clinical manifestations, neuroimages, and functional outcomes at discharge were collected and analyzed. Association between altitude and initial severity, neuroimages, and short-term prognosis of ICH were also investigated. A total of 724 patients with 400 lowlanders and 324 highlanders were enrolled. Compared with patients from the plain, those at high altitude were characterized by more severe preliminary manifestations (P < 0.0001), larger hematoma volume (P < 0.001) and poorer short-term functional outcome (P < 0.0001). High altitude was independently associated with dependency at discharge (adjusted P = 0.024), in-hospital mortality (adjusted P = 0.049) and gastrointestinal hemorrhage incidence (adjusted P = 0.017). ICH patients from high altitude suffered from more serious initial manifestations and worse short-term functional outcome than lowlanders. Control of blood pressure, oxygen supplementation and inhibition of inflammation may be critical for ICH at high altitude.

Initial and follow-up high-resolution vessel wall MRI study of spontaneous cervicocranial artery dissection.

To explore the factors associated with ischemic stroke secondary to spontaneous cervicocranial artery dissection (sCCAD) and evaluate the initial imaging markers related to outcomes. Initial and follow-up high-resolution vessel wall MRI (VW-MRI) in consecutive patients with sCCAD were retrospectively analyzed. The associations of clinical and imaging factors and variants of the circle of Willis (COW) with ischemic stroke were evaluated using binary logistic regression analyses. The anatomical outcomes were categorized as complete, partial, and no remodeling based on changes of the vessel wall and lumen. Ordinal logistic regression analysis was used to assess associations between initial features and outcomes. A total of 115 dissected arteries (79 strokes, 36 non-strokes) were detected in 103 patients. Follow-up VW-MRI was available in 46 patients (44.7%, with 51 vessels), with a median interval of 8.5months. Pseudoaneurysm (odd ratio [OR], 0.178; 95% confidence interval [CI], 0.039-0.810; p = 0.026) tended to rarely cause ischemic stroke, while intraluminal thrombus (OR, 5.558; 95% CI, 1.739-17.765; p = 0.004), incomplete COW (OR, 9.309; 95% CI, 2.122-40.840; p = 0.003), and partial complete COW (OR, 4.463; 95% CI, 1.211-16.453; p = 0.025) were independently associated with stroke occurrence. Furthermore, the presence of double lumen (OR, 5.749; 95% CI, 1.358-24.361; p = 0.018) and occlusion (OR, 12.975; 95% CI, 3.022-55.645; p = 0.001) were associated with no remodeling of sCCAD. Multiple initial factors were found to be related to stroke occurrence and anatomical outcomes of sCCAD. High-resolution VW-MRI may provide valuable insights into the pathophysiology and evolution of sCCAD. Initial and follow-up high-resolution vessel wall MRI may help elucidate the pathophysiology of spontaneous cervicocranial artery dissection and provide important insights into the evolution and further facilitate the optimal management of patients with spontaneous cervicocranial artery dissection. • Clinical and imaging factors, as well as the status of primary collateral circulation, are associated with ischemic stroke secondary to spontaneous cervicocranial artery dissection. • The follow-up high-resolution vessel wall MRI provides valuable insights into the long-term evolution and anatomical outcomes of spontaneous cervicocranial artery dissection. • The high-resolution vessel wall MRI features related to ischemic stroke and anatomical outcomes may further facilitate the optimal management of patients with spontaneous cervicocranial artery dissection.

Embedding epistemic insight (EI) in teacher training programmes in English universities: barriers and how to overcome them

ABSTRACT Entrenched compartmentalisation of curriculum subjects and ‘teaching to the test’ can leave students with limited understanding of the nature and interaction of disciplines. The necessity of developing future teachers’ epistemic insight (EI) and equipping them with strategies to address the gaps between subjects has been pushed to the fore by challenges that emerged during the pandemic. This article examines the extent to which epistemic insight is understood by Initial Teacher Education tutors and features in their programmes as well as their recommendations for increasing the inclusion of multidisciplinary approaches in education, based on qualitative and quantitative research. While initial findings revealed a poor understanding of EI, its importance was broadly acknowledged, and the follow-up survey revealed progress in the form of greater engagement with and inclusion of EI by tutors in teacher education. This research was done in the context of a new inspection framework distinguishing substantive and disciplinary knowledge.

Feature selection for image classification using ESSO

Feature selection is one of the major components of the data processing flow that correctly selects real-time entities for categorization. It is utilized in numerous research fields, including the machine learning approach. Feature selection and classification are beneficial to the processing of biomedical data in high-importance, high-dimensional datasets. Due to numerous obstacles in research, the current implementations are inadequate for predicting classification accuracy. To handle the classification challenge, we require dedicated neural network and classification model approaches. Initial features are selected using a method called "fuzzy c-means clustering with rough set theory" and are subsequently classified using "support vector machines." The learning machines' accuracy is seriously harmed by irrelevant and redundant features. The curse of dimensionality makes it difficult to locate data clusters in high-dimensional space. Data in the irrelevant dimensions may cause a lot of noise when the dimensionality grows. In addition, the current approach has a significant flaw: it is extremely time-consuming. The proposed solution used efficient feature subset selection in high-dimensional data to fix these problems. To begin with, our recommended method used Enhanced Social Spider Optimization (ESSO) computation, in which the standard Social Spider Optimization is improved with the aid of optimal radial based calculation to select the best highlights. When categorizing data, the Optimal Radial Basis Function Neural Network (ORBFNN) is used. Methods for calculating Artificial Bee Colony (ABC) are used to streamline RBFNN's sufficiency in characterizing smaller scale show information.

Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things

Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training phase, but for most traffic datasets, it is difficult to obtain label information in practical applications. Although unsupervised learning does not rely on labels, its classification accuracy is not high, and the number of data classes is difficult to determine. This paper proposes an unsupervised NTC method based on adversarial training and deep clustering with improved network traffic classification (NTC) and lower computational complexity in comparison with the traditional clustering algorithms. Here, the training process does not require data labels, which greatly reduce the computational complexity of the network traffic classification through pretraining. In the pretraining stage, an autoencoder (AE) is used to reduce the dimension of features and reduce the complexity of the initial high-dimensional network traffic data features. Moreover, we employ the adversarial training model and a deep clustering structure to further optimize the extracted features. The experimental results show that our proposed method has robust performance, with a multiclassification accuracy of 92.2%, which is suitable for classification with a large number of unlabeled data in actual application scenarios. This paper only focuses on breakthroughs in the algorithm stage, and future work can be focused on the deployment and adaptation in practical environments.

Comparing Clinical and Radiological Features in Familial and Sporadic Multiple Sclerosis.

Objective To compare the initial presentation, clinical features, disease courses, and radiological parameters between familial multiple sclerosis (fMS) and sporadic multiple sclerosis (sMS) to determine if the two represent distinct clinical entities. Methods This retrospective study was conducted at the Neurology Clinic at Kocaeli University Hospital. Records of 114 fMS and 150 sMS patients, aged 18-65, diagnosed based on either the Poser criteria or the McDonald 2001 criteria were analyzed. Radiological data and Expanded Disability Status Scale (EDSS) evaluations were conducted by a specialist neurologist. Variables included age at MS onset, first symptoms, relapses, EDSS scores at diagnosis and last examination, and MRI findings. Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 28, Armonk, NY) was utilized for data analysis. Results Both fMS and sMS groups were comparable in age (43.55±12.50 and 42.35±10.61 years, respectively) and gender distribution (females: fMS 71.9%, sMS 71.3%). No significant difference was noted regarding disease onset age (fMS 29.83±10.77, sMS 30.42±9.7). Age of onset, final EDSS, and relapse rate didn't significantly vary among sMS, fMS with first-degree relatives having MS (fMS(1)), and fMS with second or third-degree relatives having MS (fMS(2)). The fMS group showed a significantly higher incidence of initial spinal cord lesions on MRI compared to the sMS group (38.6% vs. 17.3%; p<0.001). Within the fMS group, the presence of spinal cord lesions on initial MRI correlated with a higher relapse rate and elevated initial and final EDSS scores. Conclusion Despite overarching similarities between fMS and sMS, spinal cord lesions' prevalence and implications in fMS may point to a genetic underpinning warranting in-depth exploration.

Autoimmune diseases and their various manifestations in the oral cavity – a systematic review

Introduction and aim. Oral manifestation of the disorder is the leading cause of common initial features of most autoimmune diseases. Therefore, this study aimed to present different oral manifestations of selected autoimmune diseases. Material and methods. We systematically reviewed the etiology, signs and symptoms, oral manifestations, epidemiology, diagnosis, treatment plan, and prognosis. We searched the articles on PubMed, Google Scholar and Web of Science for the following search term: Behcet’s disease, lichen planus, mucous membrane pemphigoid and bullous pemphigoid, pemphigus vulgarisms and paraneoplastic pemphigus, rheumatoid arthritis, Sjögren’s syndrome, IgG4-related disease, systemic lupus erythematous, and granulomatosis with polyangitis. Analysis of the literature. We conducted that the disorder’s oral manifestation causes most autoimmune illnesses’ earliest symptoms. Conclusion. Clinical-pathological is a piece of requisite knowledge for the dentist to recognize and diagnose in the early phase of the symptoms.

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis.

Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q+, QH+, ELUMO, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs' molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.

CLL-010 The Correlation of Initial Hematological Features With Advanced Stage in Chronic Lymphocytic Leukemia (CLL) in Kurdistan Region of Iraq

CLL-010 The Correlation of Initial Hematological Features With Advanced Stage in Chronic Lymphocytic Leukemia (CLL) in Kurdistan Region of Iraq

Breast cancer prediction by ensemble meta-feature space generator based on deep neural network

BackgroundClassifying ultrasound images is one of the quickest approaches to diagnose breast cancer. In recent years, the medical community has increasingly turned to deep learning-based methods, which have demonstrated superior performance compared to other existing methods for most computer vision tasks. Since deep learning approaches suffer from over-fitting problem, the main challenge in the medical image analysis is the lack of enough available dataset to train deep learning models to extract efficient features. Many existing methods rely on data augmentation techniques to address the issue of over-fitting especially in imbalanced datasets. However, the process of finding appropriate data augmentation methods often involves trial and error, and it can be time-consuming. Therefore, there is a need to develop an algorithm that can automatically generate effective features, and minimize the reliance on a large amount of data to overcome over-fitting. MethodIn this study, we introduced a novel ensemble meta-feature space generator (EMFSG-Net) for the classification of breast ultrasound images. We first employed a transfer learning approach to obtain initial features from raw images. Then we applied our method to create a more efficient feature space from the initial features. Our method is inspired by ensemble methods that aim to minimize bias and variance errors. Ensemble methods are widely used in previous works based on classification models. The novelty of our approach is to adapt these techniques to regression-based models, which enables us to construct a meta-feature generator capable of producing an effective feature space. This method offers an effective solution to address the overfitting problem commonly encountered in deep learning for medical image processing. By obtaining more suitable features, it significantly improves the classification performance. Notably, this method eliminates the need for data augmentation, which saves time by avoiding the search for appropriate data augmentation techniques. This approach demonstrates its efficacy specifically in addressing imbalanced datasets, which are characterized by a disproportionate distribution of class labels. ResultWe have evaluated our model on BUSI dataset, a collection of breast ultrasound images that is widely used in scientific researches to detect breast cancer. We have achieved the accuracy of 97.96% and F1-score of 96.2% on the dataset. ConclusionThe experimental results show that the proposed model has increased 4.8% of the classification F1-score compared to other feature extraction and deep learning methods.

Word self-update contrastive adversarial networks for text-to-image synthesis

Synthesizing realistic fine-grained images from text descriptions is a significant computer vision task. Although many GANs-based methods have been proposed to solve this task, generating high-quality images consistent with text information remains a difficult problem. These existing GANs-based methods ignore important words due to the use of fixed initial word features in generator, and neglect to learn semantic consistency between images and texts for discriminators. In this article, we propose a novel attentional generation and contrastive adversarial framework for fine-grained text-to-image synthesis, termed as Word Self-Update Contrastive Adversarial Networks (WSC-GAN). Specifically, we introduce a dual attention module for modeling color details and semantic information. With a new designed word self-update module, the generator can leverage visually important words to compute attention maps in the feature synthesis module. Furthermore, we contrive multi-branch contrastive discriminators to maintain better consistency between the generated image and text description. Two novel contrastive losses are proposed for our discriminators to impose image-sentence and image-word consistency constraints. Extensive experiments on CUB and MS-COCO datasets demonstrate that our method achieves better performance compared with state-of-the-art methods.

Property graph representation learning for node classification

Graph representation learning (graph embedding) has led to breakthrough results in various machine learning graph-based applications such as node classification, link prediction and recommendation. Many real-world graphs can be characterized as the property graphs, because besides the structure information, there exists rich property information related to each node in the graphs. Many existing graph representation learning methods—e.g. random walk-based methods like DeepWalk and Node2vec, focus only on the structure of graph for learning the node embedding. Although graph representation learning based on neural networks (e.g. typical GNN methods such as GraphSAGE) uses the property of nodes as the initial features of nodes and then aggregates feature information of the neighbours, their limitation is that the neighbourhood of a node is considered to be uniform—i.e. there is no way to differentiate among neighbours of a node when learning a node embedding. Additionally, their definition of neighbourhood is local, i.e. only nodes connected to the current node are considered as neighbours. Hence, those methods fail to capture implicit/latent relationships among nodes, which are implicit in the given structure. In this study, our aim is to improve the performance of graph representation learning methods on property graphs. We present a new framework called Enhanced Property Graph Embedding (EPGE)—a graph representation learning framework to address above-mentioned limitations. Our proposed framework relies on the notion of latent neighbourhood, as well as systematic sampling of neighbouring nodes to obtain better representation of the nodes. The experimental results on five publicly available graph datasets demonstrate that EPGE outperforms state-of-the-art baselines for the task of node classification. We further evaluate the superiority of our proposed formulation by defining a novel quantitative metric to measure the usefulness of the sampled neighbourhood in the graph.

Экранные игры – иллюзия героизма и бессмертия в негероическую эпоху

The topic of the article is related to the concept of media reality as a matrix for the construction of cultural reality and corporeality of the modern individual. A number of philosophical views on the peculiarities of the organiza-tion of virtual game space, as well as its influence on the perception and consciousness of gamers by purpose-fully forming emotions and motivation of game achievements corresponding to game behavior are compared. Particular attention is paid to the study of the basic categories of the game, such as initial conditions, rules and features of the game chronotope, followed by the analysis of the illusory nature of the basic existentials of the hero and immortality, as well as the nature of this illusory nature. Based on the results of the study, the role of the screen as the main tool for creating the emotional attractiveness of the game is assessed, within which the basic types of game action and positioning are highlighted. In conclusion, the features of the screen as a means of organizing (interface) of gamer’s interaction with the virtual space of the game are considered.