Art Approaches Research Articles

Graph Laplacian Diffusion Localization of Connected and Automated Vehicles

In this paper, we design distributed multi-modal localization approaches for Connected and Automated vehicles. We utilize information diffusion on graphs formed by moving vehicles, based on Adapt-then-Combine strategies coupled with the Least-Mean-Squares and the Conjugate Gradient algorithms. We treat the vehicular network as an undirected graph, where vehicles communicate with each other by means of Vehicle-to-Vehicle communication protocols. Connected vehicles perform cooperative fusion of different measurement modalities, including location and range measurements, in order to estimate both their positions and the positions of all other networked vehicles, by interacting only with their local neighborhood. The trajectories of vehicles were generated either by a well-known kinematic model, or by using the CARLA autonomous driving simulator. The proposed distributed and diffusion localization schemes significantly reduced the GPS error and do not only converged to the global solution, but they even outperformed it. Extensive simulation studies highlight the benefits of the various methods, which in turn outperform other state of the art approaches. The impact of the network connections and the network latency are also investigated.

SCDC-Net: Skin Cancer Detection and Classification using Deep Q Neural Network with Hybrid Features

Skin cancer is one of the most severe malignancies that may affect individuals of all ages. It can affect people of all races and ethnicities. Thus, early detection and treatment of skin cancer have the potential to save millions of lives. Because of the high degree of similarity between skin lesions, conventional machine learning approaches failed to provide the highest possible detection and classification accuracy in the skin cancer detection and classification (SCDC) system. To achieve the robust performance, this work is presented a SCDC-Net by employing deep learning, transfer learning models with hybrid features. Initially, UG-Net based transfer learning model is developed by combining generative adversarial networks (GAN) and U-Net. Here, UG-Net is used to remove hair from skin lesions and also preprocess enhances lesion. In addition, Hybrid U-Net (HU-Net) is used to localize the disease effected area of skin cancer. Further, various hybrid features are extracted from the segmented output by employing the gray-level cooccurrence matrix (GLCM) based texture characteristics, the discrete wavelet transform (DWT)-based low-level features, and the computation of image statistics for the texture, low-level, and colour features, respectively. In order to ensure the highest possible performance of the system, a deep q neural network (DQNN) was constructed for the classification of skin cancer using GLCM, DWT, and statistical colour features. The simulation results shows that the proposed method resulted in superior subjective and objective performance as compared to state of art approaches on ISIC-2019 public challenge dataset.

How innovations in methodology offer new prospects for volume electron microscopy

Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.

Managing communities in decentralised social environments

Many decentralised systems can be represented as graphs, and the detection of their community structure can uncover important properties. Several community detection algorithms have been proposed, however, only a few solutions are suitable for detecting and managing communities in a distributed and highly dynamic environment. This lacking is mainly due to the difficulty of defining self-organising solutions in the presence of a high rate of dynamism. The main contribution of this paper is DISCO, a distributed protocol for community detection and management in a Peer-to-Peer dynamic environment. Our approach is mainly targeted to Decentralised Online Social Networks (DOSNs), but it can be applied in other distributed scenarios. In the context of DOSNs, DISCO allows to discover communities in the local social network of a user, named ego network, and to manage their evolution over time. DISCO is based on a Temporal Trade-off approach and exploits a set of super-peers for the management of the communities. The paper presents an extensive evaluation of the proposed approach based on a dataset gathered from Facebook and shows the ability of DISCO to orchestrate a set of nodes to detect and manage communities in a highly dynamic and decentralised environment. The paper also proposes a comparison with a state of the art approach, showing that it is capable of reducing the number of critical community lifecycle events by over 25%, and reducing the average loading factor by up to 50%.Graphical abstract

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection

We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson’s correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.

Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering-based methods not only view the transformations of the same image as “positive” samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations’ discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations’ neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs’ neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets.

A Deep Graph Cut Model For 3D Brain Tumor Segmentation.

Brain tumor segmentation plays a key role in tumor diagnosis and surgical planning. In this paper, we propose a solution to the 3D brain tumor segmentation problem using deep learning and graph cut from the MRI data. In particular, the probability maps of a voxel to belong to the object (tumor) and background classes from the UNet are used to improve the energy function of the graph cut. We derive new expressions for the data term, the region term and the weight factor balancing the data term and the region term for individual voxels in our proposed model. We validate the performance of our model on the publicly available BRATS 2018 dataset. Our segmentation accuracy with a dice similarity score of 0.92 is found to be higher than that of the graph cut and the UNet applied in isolation as well as over a number of state of the art approaches.

FisheyeHDK: Hyperbolic Deformable Kernel Learning for Ultra-Wide Field-of-View Image Recognition

Conventional convolution neural networks (CNNs) trained on narrow Field-of-View (FoV) images are the state-of-the art approaches for object recognition tasks. Some methods proposed the adaptation of CNNs to ultra-wide FoV images by learning deformable kernels. However, they are limited by the Euclidean geometry and their accuracy degrades under strong distortions caused by fisheye projections. In this work, we demonstrate that learning the shape of convolution kernels in non-Euclidean spaces is better than existing deformable kernel methods. In particular, we propose a new approach that learns deformable kernel parameters (positions) in hyperbolic space. FisheyeHDK is a hybrid CNN architecture combining hyperbolic and Euclidean convolution layers for positions and features learning. First, we provide intuition of hyperbolic space for wide FoV images. Using synthetic distortion profiles, we demonstrate the effectiveness of our approach. We select two datasets - Cityscapes and BDD100K 2020 - of perspective images which we transform to fisheye equivalents at different scaling factors (analogue to focal lengths). Finally, we provide an experiment on data collected by a real fisheye camera. Validations and experiments show that our approach improves existing deformable kernel methods for CNN adaptation on fisheye images.

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder.

In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.

Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images

Hyperspectral Remote Sensing (HRS) is an emergent, multidisciplinary paradigm with several applications, which are developed on the basis of material spectroscopy, radiative transfer, and imaging spectroscopy. HRS plays a vital role in agriculture for crops type classification and soil prediction. The recently developed artificial intelligence techniques can be used for crops type classification using HRS. This study develops an Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crop Type Classification (ISCO-DTLCTC) model. The ISCO-DTLCTC technique comprises initial preprocessing step to extract the region of interest. The information gain-based feature reduction technique is employed to reduce the dimensionality of the original hyperspectral images. In addition, a fusion of 3 deep convolutional neural networks models namely, VGG16, SqueezeNet, and Dense-EfficientNet perform feature extraction process. Furthermore, sine cosine optimization (SCO) algorithm with Modified Elman Neural Network (MENN) model is applied for crops type classification. The design of SCO algorithm helps to proficiently select the parameters involved in the MENN model. The performance validation of the ISCO-DTLCTC model is carried out using benchmark datasets and the results are inspected under several measures. Extensive comparative results demonstrated the betterment of the ISCO-DTLCTC model over the state of art approaches with maximum accuracy of 99.99%.

Designing Sustainable Craft Curricula: Balancing Academic and Practical Training

AbstractSince craft education was absorbed into higher education (HE), the concept of crafts and the demographic profiles of crafts practitioners have evolved, yet little is known about the extent to which craft programmes in HE are adapting to 21st‐century changes and trends. This study aimed to investigate current HE crafts curricula in South Korea, focusing on students' perception of the efficacy of academic crafts training, and to identify the knowledge and skillsets required for competence in creative industries. A content analysis of craft curricula in HE and a focus group interview and survey of students revealed that students are interested in the blurred territories between artistry and entrepreneurship and the application of crafts with the opportunities enabled by new technologies. Accordingly, HE craft programmes are reforming to meet the needs of the new generation of practitioners, but traditional discipline‐specific training with a fine arts approach and the division between studio/theory and academic/professional course content persists. We suggest that HE programmes offer more craft‐specific entrepreneurial education integrated into studio‐based courses with hands‐on practice and interdisciplinary content to meet students' needs and foster the flexible mindset required to become a craft practitioner.

STUDD: a student–teacher method for unsupervised concept drift detection

Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive models. However, this approach falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels after the model is deployed. We propose a novel approach to unsupervised concept drift detection based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the primary model’s behaviour (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.

Harmonization of multi-center diffusion tensor tractography in neonates with congenital heart disease: Optimizing post-processing and application of ComBat

Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher's ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n = 398 subjects recruited across 4 centers, with a total of n = 763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure.

A Culturally Informed Trauma Therapy Approach for Muslim Refugee Families

Refugees arriving in western countries from predominantly Muslim countries, who have already been exposed to severe traumatic experiences in their country of origin, experience further stress during the resettlement process in the host country. Although the number of Muslim refugees is increasing in the U.S. and Europe, the existing literature is not yet adequate to determine which treatments are effective for traumatized Muslim refugee families. Since inappropriate treatment approaches for this population may result cause ineffective or even negative therapy outcomes, this study aimed to develop a culturally responsive treatment model. Specifically, it offers an integrated family-based therapy approach for refugee families, which is influenced by narrative, art, and narrative exposure approaches. This integrated family therapy model consists of 12 weekly sessions in four phases: a) diagnostic interview; b) narration of the life story; c) exposure to the trauma; and d) re-narration and reprocessing of the traumatic events. Future studies should test the feasibility and effectiveness of this integrated model with randomized controlled trials as the implementation of such treatment models is essential for developing a culturally sensitive treatment model for Muslim refugees.

Hierarchical Novelty Detection for Traffic Sign Recognition.

Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy.

Preconception period in women and men undergoing Assisted Reproduction: A gender approach for reproductive health

ObjectiveTo evaluate lifestyle and risk factors playing a role in increasing the burden of cardiovascular diseases and to increase attention of clinicians who should envision a broader preconception approach in ART, not only in women but also in men. Study designIn this cross-sectional study we investigated 90 Caucasian couples, referred to the Internal Medicine Clinic at the Assisted Reproductive Technology Centre, in order to better define in preconceptional period their cardiovascular risk profile, based on metabolic parameters and lifestyle behaviours. ResultsWe observed two-fold increase of overweight in men in comparison to women (p = 0.006). Values of waist ≥ 94 cm in men and ≥ 80 cm in women were present in 53.3% of men and 32.2% of women (p = 0.007). Similarly, values of WHR according to gender cut-off, were present in 64.4% of men and in 32.2% of women (p < 0.0001). In men we observed a significantly higher prevalence of hypertension (p = 0.02), significantly lower HDL-c (p = 0.001) and higher levels of total cholesterol (p = 0.01), LDL-c and triglycerides (p = 0.001). Sedentary behaviour was observed in about 60% of both genders. Alcohol consumption was reported by 42.2% of men and 26.7% of women (p = 0.04) and smokers were more prevalent among women (26.7%) than men (24.4%). We observed a lower adherence to Mediterranean Diet related to consumption of red meat and meat products (p = 0.02), as well as of legumes consumption (p = 0.01) in men, whereas sweet/pastries (p = 0.05) and fruits (p = 0.06) in women. Men and women with normal BMI, waist and WHR, showed a higher Mediterranean diet adherence in comparison to overweight/obese study population(p < 0.001). Finally, higher education level predicted the higher Mediterranean diet adherence in both gender (p = 0.01 for men, p = 0.03 for women). ConclusionsThis study paid attention on preconceptional health in couples planning ART. Women have a greater biological role in childbearing, whereas the role of men is underestimated. The need to conceive through ART offers a window of opportunity not only to evaluate cardiovascular profile and lifestyle factors in couples prior to conception, but also to manage comorbidities and promote health behaviours in order to improve fertility and health outcomes in both women and men at short and long-term.

Uslub Tasybih in Malay Figurative Language in the Novel Suriati from A Fine Arts Approach

One of the elements that liven up the storyline of a piece of work is uslub. Uslub or language style is the pulse that moves the process of writing. A language style that is not appropriate or ambiguous will affect the storyline. In studying a text with Islamic elements, it is important that the uslub of the text matches the content. The analysis of uslub is important in order to understand the message of a particular text. Therefore, this study analyses uslub tasybih, in the context of Malay figurative language from the novel Suriati (1993) written by Azizi Haji Abdullah from the Fine Arts Approach through the Religious Methodology contained in the Malay Methodology Theory (1989). This study uses the Malay Methodology as the analytical framework. It analyzes the relationship between uslub tasybih in the context of Malay Figurative Language and the literary work with Islamic elements in the novel Suriati. The focus of this study is to show whether the uslub tasybih in the context of Malay Figurative Language that were applied match the novel studied and reflect the aspects of Islamic beauty, thus conforming to the approach adopted in this study. The findings show that the uslub tasybih in the context of Malay Figurative Language have a direct relationship in Islamic literary work and highlight the Fine Arts Approach features which emphasize the aspect of beauty based on Islamic aesthetic values. Therefore, uslub tasybih should be in accordance with the requirements of Islam in the pursuit of perfection, civility, and order in all activities carried out by Muslims.

Accounting and art: the art commissions of the confraternities during the 15th and 16th centuries

PurposeThis paper aims to investigate the link between accounting, religion and art to understand the managerial approach of the Confraternity of Corpus Domini of Urbino and the phenomenon of art commissioning between 1465 and 1513.Design/methodology/approachThis study is based on the interpretive historical method used to understand, through accounting, the managerial role of confraternities within the economic activity of art commissioning. To this purpose, the present analysis is based on a primary source, represented by the book of revenues and expenses, named B1 (1465–1513).FindingsThe analysis has provided evidence of the role of Urbino’s Confraternity in supporting art commissioning and its capacity to invest significant resources in favor of the social, religious and institutional environment of the time. Results show the connection between Urbino’s Confraternity and painters based on their commissioning agreements, the relation between painters and the Ducal Court and, finally, the role of the Duke of Urbino in funding the Confraternity’s initiatives for painters. Thus, this study highlights the major role played by Urbino’s Confraternity in art commissioning, an instrumental part of the Confraternity’s mission.Originality/valueThis paper presents a unique case study that brings out the managerial approach in art commissioning through accounting documents which make activities and links visible.

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

SummaryNowadays, intrusion detection systems (IDSs) enabled with computational intelligence for electing the suitable classifiers to learn the patterns of various types of network attacks are under study. Selection mechanism of single or ensemble classifiers based on performance and ranking is to be enabled in the IDS framework to make accurate pattern predictions. A new anomaly based network intrusion detection system framework is proposed using context adaptive classification through the technique for order of preference by similarity to ideal solution (TOPSIS) ranking mechanism with hierarchy based chi‐square and bat algorithms for feature selection. Parameters like accuracy, false positive rate (FPR), and classification model building time are explored for choosing the best and situation aware classifiers using TOPSIS. For experimentation, NSL‐KDD and UNSW‐NB15 datasets are used. The proposed system opts decision tree (DT) algorithm for NSL‐KDD and produces 98.77% accuracy, 0.03% FPR with 8 features. Ensemble learner is selected for the UNSW‐NB15 dataset using DT and support vector machine classifiers with 9 features. Combined classifier predicts 89.43% accuracy and 3.215% FPR. Experimentation on the proposed methodology with the state of the art approaches produces promising results.

Self‐supervised monocular depth estimation via asymmetric convolution block

AbstractWithout the dependence of depth ground truth, self‐supervised learning is a promising alternative to train monocular depth estimation. It builds its own supervision signal with the help of other tools, such as view synthesis and pose networks. However, more training parameters and time consumption may be involved. This paper proposes a monocular depth prediction framework that can jointly learn the depth value and pose transformation between images in an end‐to‐end manner. The depth network creatively employs an asymmetric convolution block instead of every square kernel layer to strengthen the learning ability of extracting image features when training. During inference time, the asymmetric kernels are fused and converted to the original network to predict more accurate image depth, thus bringing no extra computations anymore. The network is trained and tested on the KITTI monocular dataset. The evaluated results demonstrate that the depth model outperforms some State of the Arts (SOTA) approaches and can reduce the inference time of depth prediction. Additionally, the proposed model performs great adaptability on the Make3D dataset.