Reviewerly/exHarmony huggingface dataset:
Artificial CT images can enhance variation of case images in diagnostic radiology skills training

Abstract Objectives We sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided. Methods Artificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images (512 × 512 primary and control image sets) blended in with original images, using both quantitative metrics and expert opinion. We further assessed if pathology characteristics in the artificial images can be manipulated. Results Primary and control artificial images attained an average objective similarity of 0.78 ± 0.04 (ranging from 0 [entirely dissimilar] to 1[identical]) and 0.76 ± 0.06, respectively. Five radiologists with experience in chest and thoracic imaging provided a subjective measure of image quality; they rated artificial images as 3.13 ± 0.46 (range of 1 [unrealistic] to 4 [almost indistinguishable to the original image]), close to their rating of the original images (3.73 ± 0.31). Radiologists clearly distinguished images in the control sets (2.32 ± 0.48 and 1.07 ± 0.19). In almost a quarter of the scenarios, they were not able to distinguish primary artificial images from the original ones. Conclusion Artificial images can be generated in a way such that they blend in with original images and adhere to anatomical constraints, which can be manipulated to augment the variability of cases. Critical relevance statement Artificial medical images can be used to enhance the availability and variety of medical training images by creating new but comparable images that can blend in with original images. Key points • Artificial images, similar to original ones, can be created using generative networks. • Pathological features of artificial images can be adjusted through guiding the network. • Artificial images proved viable to augment the depth and broadening of diagnostic training. Graphical Abstract
USE OF QUIZLET OUTSIDE CLASS IN ENHANCING ESFL LEARNERS’ VOCABULARY LEARNING: A CASE STUDY

Quizlet, one of the applications of Mobile assisted language learning (MALL) is a free tool to learn vocabulary, concepts, and terminology through flashcards and fun games to help learners remember easily and remember for a long time. This study aims at investigating the use of the Quizlet outside classroom to improve the English vocabulary learning capacity for 11th grade students at Hoang Hoa Tham Upper Secondary School - Danang city, Vietnam. The research employed a mixed study method using quantitative and qualitative data. The quantitative data were gathered from vocabulary tests involving 81 students and questionnaires, while the qualitative data were collected through interviews with 10 students. The results showed that Quizlet changed the vocabulary scores significantly compared to traditional methods. Most student participants agree that Quizlet is a useful tool for learning new vocabulary. In addition, it is also suggested that students should use a combination of traditional methods and Quizlet to gain the best results. Regardless of students’ perceptions, Quizletencouraged them to learn vocabulary at great frequency. They also indicated that they had high emotional engagement in using Quizlet. Quizlet was user-friendly and helped them to learn vocabulary fast. Therefore, students can use Quizlet as a useful tool in learning English vocabulary and improving their vocabulary memorization.
Effectiveness of the Krotov method in finding controls for open quantum systems

Abstract We apply the Krotov method (KM) for open and closed quantum systems to find optimized controls to manipulate qubit/qutrit systems in the presence of the external environment. In the case of unitary optimization, the KM is first applied to a quantum system neglecting its interaction with the environment. The resulting controls from the unitary optimization are then used to drive the system along with the environmental noise. In the case of non-unitary optimization, the KM already takes into account the noise during the optimization process. We consider two distinct computational tasks: target-state preparation and quantum gate implementation. These tasks are carried out in simple qubit/qutrit systems and also in systems presenting leakage states. For the state preparation cases, the controls from the non-unitary optimization outperform the controls from the unitary optimization. However, as we show here, this is not always true for the implementation of quantum gates. There are some situations where the unitary optimization performs equally well compared to the non-unitary optimization. We verify that these situations correspond to either the absence of leakage states or to the effects of dissipation being spread uniformly over the system, including non-computational levels. For such cases, the quantum gate implementation must cover the entire Hilbert space and there is no way to dodge dissipation. On the other hand, if the subspace containing the computational levels and its complement are differently affected by dissipation, the non-unitary optimization becomes effective.
Looking for related posts on GitHub discussions

Software teams increasingly adopt different tools and communication channels to aid the software collaborative development model and coordinate tasks. Among such resources, software development forums have become widely used by developers. Such environments enable developers to get and share technical information quickly. In line with this trend, GitHub announced GitHub Discussions—a native forum to facilitate collaborative discussions between users and members of communities hosted on the platform. Since GitHub Discussions is a software development forum, it faces challenges similar to those faced by systems used for asynchronous communication, including the problems caused by related posts (duplicated and near-duplicated posts). These related posts can add noise to the platform and compromise project knowledge sharing. Hence, this article addresses the problem of detecting related posts on GitHub Discussions. To achieve this, we propose an approach based on a Sentence-BERT pre-trained general-purpose model: the RD-Detector . We evaluated RD-Detector using data from three communities hosted in GitHub. Our dataset comprises 16,048 discussion posts. Three maintainers and three Software Engineering (SE) researchers manually evaluated the RD-Detector results, achieving 77–100% of precision and 66% of recall. In addition, maintainers pointed out practical applications of the approach, such as providing knowledge to support merging the discussion posts and converting the posts to comments on other related posts. Maintainers can benefit from RD-Detector to address the labor-intensive task of manually detecting related posts.
Visualizing software refactoring using radar charts

Abstract Refactoring tools have advanced greatly and are being used in many large projects. As a result, a great deal of information is now available about past refactoring and its effects on the source code. However, when multiple refactoring is performed at once, it becomes more difficult to analyze their impact. Refactoring visualization can help developers create more maintainable code that is easier to understand and modify over time. Although there is an increasing interest in visualizing code changes in software engineering research, there has been relatively little research on visualizing the process of refactoring. In this paper, we propose a Radar Chart Refactoring Visualization (RcRV) approach to visualize software refactoring of source code across multiple software releases. Radar charts are a form of 2D visualization that can show multiple variables on a single chart. The RcRv receives input from developers or through refactoring identification tools, such as Ref-Finder, to generate charts. The generated charts can show the changes made during the refactoring process, highlighting areas of the trend of refactoring over evolution for multiple refactoring, multiple methods, and multiple classes. The evaluation study conducted to assess the usefulness of the RcRV tool has shown that the proposed tool is useful to developers, appealing, and easy to use. The proposed method of visualization can be beneficial for developers and maintainers to detect design violations and potential bugs in the code, thus saving time and effort during the development and maintenance process. Therefore, this research presents a significant contribution to the software engineering field by providing developers with an efficient tool to enhance code quality and maintainability.
SDFPoseGraphNet: Spatial Deep Feature Pose Graph Network for 2D Hand Pose Estimation

In the field of computer vision, hand pose estimation (HPE) has attracted significant attention from researchers, especially in the fields of human–computer interaction (HCI) and virtual reality (VR). Despite advancements in 2D HPE, challenges persist due to hand dynamics and occlusions. Accurate extraction of hand features, such as edges, textures, and unique patterns, is crucial for enhancing HPE. To address these challenges, we propose SDFPoseGraphNet, a novel framework that combines the strengths of the VGG-19 architecture with spatial attention (SA), enabling a more refined extraction of deep feature maps from hand images. By incorporating the Pose Graph Model (PGM), the network adaptively processes these feature maps to provide tailored pose estimations. First Inference Module (FIM) potentials, alongside adaptively learned parameters, contribute to the PGM’s final pose estimation. The SDFPoseGraphNet, with its end-to-end trainable design, optimizes across all components, ensuring enhanced precision in hand pose estimation. Our proposed model outperforms existing state-of-the-art methods, achieving an average precision of 7.49% against the Convolution Pose Machine (CPM) and 3.84% in comparison to the Adaptive Graphical Model Network (AGMN).
MMLKG: Knowledge Graph for Mathematical Definitions, Statements and Proofs

Abstract Nowadays, Knowledge Graphs (KGs) are important and developing in different areas. However, there is a lack of genuinely interoperable datasets representing mathematics that allow for information exchange between datasets in the Web ecosystem. In this paper, we address this matter based on the Mizar Mathematical Library (MML), a collection of articles written in the Mizar language. MML includes definitions and theorems with proofs to which authors can easily refer from newly written Mizar articles. However, extracting information directly from Mizar scripts by external projects is not very straightforward. Therefore, we propose a new data storage and retrieval approach based on the Knowledge Organization System (KOS) model and the KG concept that provides a way to organize and access knowledge. We present Mizar Mathematical Library Knowledge Graph (MMLKG), a thesaurus for describing mathematical objects. MMLKG supports semantic interoperability and allows linking data from different sources, e.g., Wikidata. Moreover, it satisfies the FAIR data principles. The data is publicly available via a Cypher endpoint.
KU-BdSL: An open dataset for Bengali sign language recognition

Sign language is a form of communication medium for speech and hearing disabled people. It has various forms with different troublesome patterns, which are difficult for the general mass to comprehend. Bengali sign language (BdSL) is one of the difficult sign languages due to its immense number of alphabet, words, and expression techniques. Machine translation can ease the difficulty for disabled people to communicate with generals. From the machine learning (ML) domain, computer vision can be the solution for them, and every ML solution requires a optimized model and a proper dataset. Therefore, in this research work, we have created a BdSL dataset and named `KU-BdSL', which consists of 30 classes describing 38 consonants ('banjonborno') of the Bengali alphabet. The dataset includes 1500 images of hand signs in total, each representing Bengali consonant(s). Thirty-nine participants (30 males and 9 females) of different ages (21-38 years) participated in the creation of this dataset. We adopted smartphones to capture the images due to the availability of their high-definition cameras. We believe that this dataset can be beneficial to the deaf and dumb (D&D) community. Identification of Bengali consonants of BdSL from images or videos is feasible using the dataset. It can also be employed for a human-machine interface for disabled people. In the future, we will work on the vowels and word level of BdSL.
NEP+: A Human-Centered Framework for Inclusive Human-Machine Interaction Development

This article presents the Network Empower and Prototyping Platform (NEP+), a flexible framework purposefully crafted to simplify the process of interactive application development, catering to both technical and non-technical users. The name "NEP+" encapsulates the platform’s dual mission: to empower the network-related capabilities of ZeroMQ and to provide software tools and interfaces for prototyping and integration. NEP+ accomplishes this through a comprehensive quality model and an integrated software ecosystem encompassing middleware, user-friendly graphical interfaces, a command-line tool, and an accessible end-user programming interface. This article primarily focuses on presenting the proposed quality model and software architecture, illustrating how they can empower developers to craft cross-platform, accessible, and user-friendly interfaces for various applications, with a particular emphasis on robotics and the Internet of Things (IoT). Additionally, we provide practical insights into the applicability of NEP+ by briefly presenting real-world user cases where human-centered projects have successfully utilized NEP+ to develop robotics systems. To further emphasize the suitability of NEP+ tools and interfaces for developer use, we conduct a pilot study that delves into usability and workload assessment. The outcomes of this study highlight the user-friendly features of NEP+ tools, along with their ease of adoption and cross-platform capabilities. The novelty of NEP+ fundamentally lies in its holistic approach, acting as a bridge across diverse user groups, fostering inclusivity, and promoting collaboration.
Development of a Persona/Scenario (P/S) Toolkit to Expand Inclusive Fashion Design Education

This study aimed to create a Persona/Scenario (P/S) toolkit for promoting inclusive fashion design. The development of the P/S toolkit drew inspiration from Lamb and Kallal"s FEA model for fashion design planning, integrating various factors such as disabilities, body types, social contexts, and diverse situations. Additionally, its design concepts consider the identities, social roles, practicality, personal tastes, and psychological needs of the user. Based on participant interviews, the P/S toolkit goes beyond conventional fashion design methods by encouraging designers to consider theﾠ diverse characteristics of users while maintaining conceptual consistency. Moreover, the P/S toolkit effectively facilitates team communication within projects focused on inclusive fashion design. As a result,, the P/S toolkit has the potential to serve as an educational resource for teaching inclusive design principles, emphasizing a comprehensive understanding of diverse user attributes. Additionally, it provides guidance for maintaining design consistency and enhancing team communication throughout the fashion design process.
Digital Optimal Robust Control

The lack of ability to determine and implement accurately quantum optimal control is a strong limitation to the development of quantum technologies. We propose a digital procedure based on a series of pulses where their amplitudes and (static) phases are designed from an optimal continuous-time protocol for given type and degree of robustness, determined from a geometric analysis. This digitalization combines the ease of implementation of composite pulses with the potential to achieve global optimality, i.e., to operate at the ultimate speed limit, even for a moderate number of control parameters. We demonstrate the protocol on IBM's quantum computers for a single qubit, obtaining a robust transfer with a series of Gaussian or square pulses in a time $T=382\text{ }\text{ }\mathrm{ns}$ for a moderate amplitude. We find that the digital solution is practically as fast as the continuous one for square subpulses with the same peak amplitudes.
A Supervised Approach for Efficient Video Anomaly Detection Using Transfer Learning

Video anomaly detection is a complex task that has numerous applications in video surveillance. It involves identifying unusual patterns or events in a video stream that deviate from the expected or typical behavior. This paper introduces a new framework for supervised video anomaly detection using transfer learning from a pre-trained model. The approach presented in this paper utilizes the MobileNetV2 architecture as a feature extractor, which is further fine-tuned using a small set of annotated data. The fine-tuned model is then utilized to classify video frames into normal or anomalous classes. The suggested methodology is evaluated on benchmark datasets and compared with state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in detecting anomalies in videos with high accuracy and low computational cost.
A Novel Keypoint Supplemented R-CNN for UAV Object Detection

Aerial imagery and remote sensing applications are excellent examples of environments that challenge current deep learning-based object detection architectures due to the images generally consisting of small objects within large, complex backgrounds. In this article, the region proposal network (RPN) of the successful Mask region-based convolutional neural network (R-CNN) architecture is supplemented with a second nonconvolutional branch to increase the number of accurately predicted regions of interest (RoIs). The additional RoIs are predicted using keypoint features unique from the Mask R-CNN backbone which better capture the existence of small objects and allow for a set of RoIs to be predicted independent of the standard Mask R-CNN feature pyramid network (FPN). The proposed architecture is evaluated on synthetic data to demonstrate the improved performance of the supplemented RPN on smaller objects and objects of changing scale. The two-branch network is then demonstrated to achieve improved detection accuracy when applied to the challenging aerial-cars and vehicle-detection datasets (VDDs).
Formulation and Implementation of a Bayesian Network-Based Model

At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.
A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation

Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.
Ambiente de realidade mista para experimentos com múltiplos robôs de baixo custo

In this paper, a mixed reality environment for navigation of multiple mobile robots is proposed. The developed environment allows the integration between virtual and real elements. To identify the real elements, ARTags markers are used, which can be obstacles, targets, robots or objectives, while the virtual ones (inserted into a virtual layer) are elements that can interact with the robots, such as virtual sensors, goals, obstacles, among others, depending on the experiment to be carried out. The Robot Operating System framework is used as a communication tool between the real and virtual layers. As a result, robots with hardware constraints acquire the skill to reach and intercept a moving goal. Three experiments are presented, highlighting the capacity of the proposed tool to provide the navigation of multiple robots in the presence of static and dynamic obstacles and targets. The obtained results validate the proposed mixed reality environment as a flexible experimental tool for the navigation of multiple robots with limited hardware.