Reviewerly/exHarmony huggingface dataset:
Analysis of color features performance using support vector machine with multi-kernel for batik classification

Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.
O MANEJO DA DOR CRÔNICA NA ATENÇÃO PRIMÁRIA DE SAÚDE NO CONTEXTO DA PANDEMIA DA COVID-19: SOB O OLHAR DE UM RESIDENTE EM SAÚDE DA FAMÍLIA

Chronic pain (CD) can be characterized by the persistence of the complaint for more than three months of the usual time for healing of the lesion, or it is still associated with chronic processes. Therefore, the present experience report aims to describe the experience lived in the management of chronic pain at the USF Alto do Céu Integrado, in the city of João Pessoa, during the Multiprofessional Residency in Family and Community Health, in the context of the Covid-19 pandemic. -19, from April 2020 to December 2021, as a way to encourage discussion to improve comprehensive care for the individual. Regarding multidisciplinary consultations, they were often not advised, whether for technical reasons or due to the high demand for respiratory symptoms within the unit and the risk of contamination. And there was an increase in cases of pain related to the musculoskeletal system, with a growing search for physiotherapy care. However, due to the structural precariousness of the unit, the increase in the number of Covid-19 cases and health restrictions, constant referrals to outpatient services were necessary. According to the reports, auriculotherapy proved to be effective in promoting the quality of life of users, not only with improvements in complaints associated with chronic pain, but also its relief or cessation. In addition, it served as a palliative until the regulatory service found a vacancy in outpatient care. However, there was still a resistant belief in biomedical models as a solution for pain management, which increased according to greater social vulnerability and lack of knowledge. Finally, to improve the understanding of the origin and permanence of pain in the user, it was necessary to improve the comprehensiveness of care, especially with regard to qualified listening, with multidisciplinary care being the most appropriate way for this to happen.
Revisiting a kNN-Based Image Classification System with High-Capacity Storage

In existing image classification systems that use deep neural networks, the knowledge needed for image classification is implicitly stored in model parameters. If users want to update this knowledge, then they need to fine-tune the model parameters. Moreover, users cannot verify the validity of inference results or evaluate the contribution of knowledge to the results. In this paper, we investigate a system that stores knowledge for image classification, such as image feature maps, labels, and original images, not in model parameters but in external high-capacity storage. Our system refers to the storage like a database when classifying input images. To increase knowledge, our system updates the database instead of fine-tuning model parameters, which avoids catastrophic forgetting in incremental learning scenarios. We revisit a kNN (k-Nearest Neighbor) classifier and employ it in our system. By analyzing the neighborhood samples referred by the kNN algorithm, we can interpret how knowledge learned in the past is used for inference results. Our system achieves 79.8% top-1 accuracy on the ImageNet dataset without fine-tuning model parameters after pretraining, and 90.8% accuracy on the Split CIFAR-100 dataset in the task incremental learning setting.
BBVD: A BERT-based Method for Vulnerability Detection

Software vulnerability detection is one of the key tasks in the field of software security.Detecting vulnerability in the source code in advance can effectively prevent malicious attacks.Traditional vulnerability detection methods are often ineffective and inefficient when dealing with large amounts of source code.In this paper, we present the BBVD approach, which treats high-level programming languages as another natural language and uses BERT-based models in the natural language processing domain to automate vulnerability detection.Our experimental results on both SARD and Big-Vul datasets demonstrate the good performance of the proposed BBVD in detecting software vulnerability.
Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing the same domain, different ontologies might be created by different domain experts. To enhance the reasoning and data handling of concepts in scientific papers, finding the best fitting ontology regarding description of the concepts contained in a text corpus. The approach presented in this work attempts to solve this by selection of a representative text paragraph from a set of scientific papers, which are used as data set. Then, using a pre-trained and fine-tuned Transformer, the paragraph is embedded into a vector space. Finally, the embedded vector becomes classified with respect to its relevance regarding a selected target ontology. To construct representative embeddings, we experiment with different training pipelines for natural language processing models. Those embeddings in turn are later used in the task of matching text to ontology. Finally, the result is assessed by compressing and visualizing the latent space and exploring the mappings between text fragments from a database and the set of chosen ontologies. To confirm the differences in behavior of the proposed ontology mapper models, we test five statistical hypotheses about their relative performance on ontology classification. To categorize the output from the Transformer, different classifiers are considered. These classifiers are, in detail, the Support Vector Machine (SVM), k-Nearest Neighbor, Gaussian Process, Random Forest, and Multilayer Perceptron. Application of these classifiers in a domain of scientific texts concerning catalysis research and respective ontologies, the suitability of the classifiers is evaluated, where the best result was achieved by the SVM classifier.
How the Processing Mode Influences Azure Kinect Body Tracking Results

The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.
Quantum Interference between Fundamentally Different Processes Is Enabled by Shaped Input Wavefunctions

This work presents a general framework for quantum interference between processes that can involve different fundamental particles or quasi-particles. This framework shows that shaping input wavefunctions is a versatile and powerful tool for producing and controlling quantum interference between distinguishable pathways, beyond previously explored quantum interference between indistinguishable pathways. Two examples of quantum interference enabled by shaping in interactions between free electrons, bound electrons, and photons are presented: i) the vanishing of the zero-loss peak by destructive quantum interference when a shaped electron wavepacket couples to light, under conditions where the electron's zero-loss peak otherwise dominates; ii) quantum interference between free electron and atomic (bound electron) spontaneous emission processes, which can be significant even when the free electron and atom are far apart, breaking the common notion that a free electron and an atom must be close by to significantly affect each other's processes. Conclusions show that emerging quantum wave-shaping techniques unlock the door to greater versatility in light-matter interactions and other quantum processes in general.
An improved UAV path planning method based on APSOvnp-APF algorithm

In recent years, unmanned aerial vehicle (UAV) path planning has been a popular research topic. This paper proposes an improved artificial potential field method based on the virtual navigation path generated by the adaptive particle swarm optimization algorithm (APSOvnp-APF). Firstly, an adaptive particle swarm optimization algorithm (APSO) which adaptively adjusts the inertia weight and learning factor is designed, and the fitness function is constructed by considering the flight characteristics of UAV. Then, a global virtual navigation path (VNP) is initially obtained by the APSO algorithm to improve the artificial potential field method(APF). The VNP provides APF with new guiding meaning and updates the gravitational rule. Furthermore, the repulsive potential field is redefined. The APSOvnp-APF algorithm solves the problems of local minimum and target unreachable in traditional APF, and achieves a more comprehensive path selection. The simulation results show that the algorithm is reliable and effective, which can formulate a smooth global optimal path for UAV.
medigan: a Python library of pretrained generative models for medical image synthesis

PurposeDeep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1) limited availability of (synthetic) datasets and (2) generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data.ApproachWe propose medigan, a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement medigan based on modular components for generative model (i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI.ResultsThe scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three medigan applications, which include (a) enabling community-wide sharing of restricted data, (b) investigating generative model evaluation metrics, and (c) improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors.Conclusionmedigan allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show medigan's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.
Decentralization of Car Insurance System Using Machine Learning and Distributed Ledger Technology

With the increase in the number of vehicle drivers, there has been a direct increase in the number of car insurance holders and claims. While the process of registration for insurance is effortless, the process cycle of claiming insurance is often very cumbersome, in view of the fact that there is the inclusivity of various intermediaries giving rise to a long-drawn-out process. This paper proposes an end to end insurance automation system which includes the user registration process, the acceptance and verification of premium payment, the insurance claim process wherein the instance segmentation machine learning model is used to identify the damaged parts of the respective user’s car, triggering the smart contracts aiding the transfer of cryptocurrency respective of the damages to the user’s wallet. Receiving and sending payments have incorporated blockchain technology, increasing the security and making the payments tamper-proof in addition to being future ready by dealing with cryptocurrency.
Contrastive Multi-Level Graph Neural Networks for Session-Based Recommendation

Session-based recommendation (SBR) aims to predict the next item at a certain time point based on anonymous user behavior sequences. Existing methods typically model session representation based on simple item transition information. However, since session-based data consists of limited users' short-term interactions, modeling session representation by capturing fixed item transition information from a single dimension suffers from data sparsity. In this paper, we propose a novel contrastive multi-level graph neural networks (CM-GNN) to better exploit complex and high-order item transition information. Specifically, CM-GNN applies local-level graph convolutional network (L-GCN) and global-level network (G-GCN) on the current session and all the sessions respectively, to effectively capture pairwise relations over all the sessions by aggregation strategy. Meanwhile, CM-GNN applies hyper-level graph convolutional network (H-GCN) to capture high-order information among all the item transitions. CM-GNN further introduces an attention-based fusion module to learn pairwise relation-based session representation by fusing the item representations generated by L-GCN and G-GCN. CM-GNN averages the item representations obtained by H-GCN to obtain high-order relation-based session representation. Moreover, to convert the high-order item transition information into the pairwise relation-based session representation, CM-GNN maximizes the mutual information between the representations derived from the fusion module and the average pool layer by contrastive learning paradigm. We conduct extensive experiments on multiple widely used benchmark datasets to validate the efficacy of the proposed method. The encouraging results demonstrate that our proposed method outperforms the state-of-the-art SBR techniques.
Galois hulls of special Goppa codes and related codes with application to EAQECCs

Galois hulls of MDS codes can be applied to construst MDS entanglement-assisted quantum error-correcting codes (EAQECCs). Goppa codes and expurgated Goppa codes (resp., extended Goppa codes) over Fqm are GRS codes (resp., extended GRS codes) when m=1. In this paper, we investigate the Galois dual codes of a special kind of Goppa codes and related codes and provide a necessary and sufficient condition for the Galois dual codes of such codes to be Goppa codes and related codes. Then we determine the Galois hulls of the above codes. In particular, we completely characterize Galois LCD, Galois self-orthogonal, Galois dual-containing and Galois self-dual codes among such family of codes. Moreover, we apply the above results to EAQECCs.
Experiences with COVID-19 case investigation and contact tracing: A qualitative analysis

Case investigation and contact tracing (CI/CT) is a critical part of the public health response to COVID-19. Individuals' experiences with CI/CT for COVID-19 varied based on geographic location, changes in knowledge and guidelines, access to testing and vaccination, as well as demographic characteristics including age, race, ethnicity, income, and political ideology. In this paper, we explore the experiences and behaviors of adults with positive SARS-CoV-2 test results, or who were exposed to a person with COVID-19, to understand their knowledge, motivations, and facilitators and barriers to their actions. We conducted focus groups and one-on-one interviews with 94 cases and 90 contacts from across the United States. We found that participants were concerned about infecting or exposing others, which motivated them to isolate or quarantine, notify contacts, and get tested. Although most cases and contacts were not contacted by CI/CT professionals, those who were reported a positive experience and received helpful information. Many cases and contacts reported seeking information from family, friends, health care providers, as well as television news and Internet sources. Although participants reported similar perspectives and experiences across demographic characteristics, some highlighted inequities in receiving COVID-19 information and resources.
Identification of patients with epilepsy using automated electronic health records phenotyping

Abstract Objective Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy. Methods The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy‐related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold‐out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision–recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping. Results Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non‐Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99–1.00) in the hold‐out testing set. Significance A machine learning‐based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large‐scale epilepsy research using EHR databases.
Deep Double Incomplete Multi-View Multi-Label Learning With Incomplete Labels and Missing Views

View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learning or incomplete multi-label learning problem. However, few works can simultaneously handle the challenging case with both the incomplete issues. In this article, we propose a new incomplete multi-view multi-label learning network to address this challenging issue. The proposed method is composed of four major parts: view-specific deep feature extraction network, weighted representation fusion module, classification module, and view-specific deep decoder network. By, respectively, integrating the view missing information and label missing information into the weighted fusion module and classification module, the proposed method can effectively reduce the negative influence caused by two such incomplete issues and sufficiently explore the available data and label information to obtain the most discriminative feature extractor and classifier. Furthermore, our method can be trained in both supervised and semi-supervised manners, which has important implications for flexible deployment. Experimental results on five benchmarks in supervised and semi-supervised cases demonstrate that the proposed method can greatly enhance the classification performance on the difficult incomplete multi-view multi-label classification tasks with missing labels and missing views.
Multiphoton blockade and antibunching in an optical cavity coupled with dipole-dipole-interacting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Λ</mml:mi></mml:math> -type atoms

We study multiphoton blockade effects in a single-mode cavity interacting with two three-level atoms in $\mathrm{\ensuremath{\Lambda}}$ configuration having position-dependent atom-field coupling. We consider the effects of dipole-dipole interaction (DDI) between the three-level atoms and show how the presence of DDI strongly influences the multiphoton blockade. For symmetric coupling of the atoms with the field, the DDI induces an asymmetry in the emission spectra as a function of pump field detuning. At positive detuning, the single-photon blockade gets stronger as a function of DDI strength, leading to photon antibunching. However, it becomes weaker at negative detuning and can also completely vanish. We show that this vanishing single-photon blockade is associated with a strong two-photon blockade, leading to two-photon bunching. Therefore, by just tuning the frequency of the pump field, we can achieve two very distinct features. We also study the effects of DDI when the atoms are asymmetrically coupled with the field and show that the proposed system exhibits two-photon bunching. We believe our results are important for the experimental realization of such systems where DDI may be present.