Reviewerly/exHarmony huggingface dataset:
Improving visual question answering for remote sensing via alternate-guided attention and combined loss

Visual question answering (VQA) for remote sensing (RS) images offers a typical multi-modal task advanced by natural language processing and computer vision technologies. Still, it remains a problem subjected to two aspects of factors. First, the RS image contains a wealth of visual elements but is rarely involved in the question; therefore, the regions humans choose to look at to answer questions differ from current attention approaches. Second, the class-imbalance problem in the existing RSVQA dataset leads the prediction to drift towards frequent answers. To address these issues, we aim to explore the intrinsic relationship between RS visual elements and text generations with imbalance compensation. Here, we propose a new method called Union Context-wise and Alternate-Guided Attention Network (UCAGAN). Our method uses a cross-modal alternative-guided attention module to map visual and textual features. Moreover, we introduce an improved multi-category loss function to compensate for the model bias caused by sample imbalance. We carried out thorough experimentation on a diverse range of datasets, demonstrating that our approach is effective and efficient, achieving state-of-the-art performance. Our work provides results that are not only correctable but also explainable, ultimately leading to the development of reliable VQA models for RS images.
Graphical User Interface

Microsoft® Visual Studio® can be used to design graphical user interfaces. This chapter shows the basics of graphical user interface design in Visual Studio.
Useful Functions to Work with Files and Directories

This chapter introduces some useful functions to work with files and directories. You will learn how to make and remove directories and copy, rename, and delete files.
Cryptocurrency and NFT investments

Criptocurrency is a digital currency in which transactions are verified and recorded by a decentralized system using cryptography, rather than by a centralized authority. This ones attracted people from simple investors to academic people to do many researches about how this market works and how can be a modern alternative to our actual currency market which is formed from currencies of each traded country. This market started to develop in august 2008 and has a real growth in 2011 when apeared other criptocurrencies near principal currency – Bitcoin. After that, in january 2021 on this market the global cap exceedes 1 trillion dollars and was in a continue growth durring the first qurter of 2021 until his global cap reached the number of 2 trillion dollars. After all, in 2015 apeared the first mention about NFT in blockchain register of Ethereum. This ones has a special particularity compared to existing cryptos until then- the lack of need to store or hold an asset, which leads to a certain degree of anonymity south by a large number of investors, and the speed of training of assets. Therefore, the goal of this is to find the main factors that determine the growth of investments in this market, especialy when this market is a little bit sensitive after the fall from May 2022, when Terra token fell in 48 hours by approximately 99.8 %. In this context, the results once again convinced us that many of investors see this market an oportunity to invest where and when they want, to make fast transactions whithout paying exorbitant bank fees, anonymity of transactions and last but not least curiosity.
Impactos Psicológicos da Pandemia de Coronavírus (COVID-19) em Profi ssionais de Saúde: Overview

his overview study aimed to synthesize the evidence available from systematic reviews on the psychological impacts on health professionals resulting from the COVID-19 pandemic, caused by the SARS-CoV-2 coronavirus. The protocol was structured based on the PRISMA 2020 recommendation and studies were searched in the PubMed, PsycINFO, SciELO, Science Direct, Web of Science and Scopus databases, including systematic review articles published up to May 2022, resulting in a fi nal sample of 32 studies. It is concluded that health professionals who are dealing with the COVID-19 pandemic were exposed to multiple psychosocial stressors and presented prevalence of depression, anxiety, insomnia/ sleep disorders, stress, PTSD symptoms, exhaustion/ burnout and fear; representing a population that is especially vulnerable and more likely to develop psychological problems during the COVID-19 pandemic. As a future agenda, it is recommended that studies be carried out that include protective factors and interventions in order to minimize these negative eff ects on the mental health of health professionals during this pandemic context, examining the long-term eff ects. Keywords: COVID-19, mental health, health personnel, systematic review, psychological consequence
: A Multilingual Humor-aided Multiparty Dialogue Generation in multimodal conversational setting

Building a multimodal humor-aided dialogue generation system in a multilingual setting is a very challenging task and it could even be more challenging in the Hindi language because of its morphological richness and free word order. Existing works only solved the problem of unimodal humorous dialogue generation, not humor-aided, in a monolingual setting. However, due to the tremendous growth in multimodal and multilingual content, there is a great demand to build multimodal humor-aided systems that support multilingual information access. To this end, in this paper, we propose a deep learning-based multimodal sentiment and emotion-aware humor-aided multiparty dialogue generation in multilingual (Hindi and English) setting. We evaluate our proposed approach (MHaDiG) on the recently released Sentiment, Humor, and Emotion-aware Multilingual Multimodal Multiparty Dataset (SHEMuD). Experimental results show the efficacy of MHaDiG and show the effect of multilingualism over monolingualism in the multimodal conversational setting.
Multi-view kernel PCA for time series forecasting

In this paper, we propose a kernel principal component analysis model for multi-variate time series forecasting, where the training and prediction schemes are derived from the multi-view formulation of Restricted Kernel Machines. The training problem is simply an eigenvalue decomposition of the summation of two kernel matrices corresponding to the views of the input and output data. When a linear kernel is used for the output view, it is shown that the forecasting equation takes the form of kernel ridge regression. When that kernel is non-linear, a pre-image problem has to be solved to forecast a point in the input space. We evaluate the model on several standard time series datasets, perform ablation studies, benchmark with closely related models and discuss its results.
Contrastive Generative Network with Recursive-Loop for 3D point cloud generalized zero-shot classification

Generalized Zero-Shot Learning (GZSL) aims to recognize objects from both seen and unseen categories by transferring semantic knowledge and merely utilizing seen class data for training. Recent feature generation methods in the 2D image domain have made great progress. However, very little is known about its usefulness in 3D point cloud zero-shot learning. This work aims to facilitate research on 3D point cloud generalized zero-shot learning. Different from previous works, we focus on synthesizing the more high-level discriminative point cloud features. To this end, we design a representation enhancement strategy to generate the features. Specifically, we propose a Contrastive Generative Network with Recursive-Loop, termed as CGRL, which can be leveraged to enlarge the inter-class distances and narrow the intra-class gaps. By applying the contrastive representations to the generative model in a recursive-loop form, it can provide the self-guidance for the generator recurrently, which can help yield more discriminative features and train a better classifier. To validate the effectiveness of the proposed method, extensive experiments are conducted on three benchmarks, including ModelNet40, McGill, and ScanObjectNN. Experimental evaluations demonstrate the superiority of our approach and it can outperform the state-of-the-arts by a large margin. Code is available at https://github.com/photon-git/CGRL
Few pixels attacks with generative model

Adversarial attacks have attracted much attention in recent years, and a number of works have demonstrated the effectiveness of attacks on the entire image at perturbation generation. However, in practice, specially designed perturbation of the entire image is impractical. Some work has crafted adversarial samples with a few scrambled pixels by advanced search, e.g., SparseFool, OnePixel, etc., but they take more time to find such pixels that can be perturbed. Therefore, to construct the adversarial samples with few pixels perturbed in an end-to-end way, we propose a new framework, in which a dual-decoder VAE for perturbations finding is designed. To make adversarial learning more effective, we proposed a new version of the adversarial loss by considering the generalization. To evaluate the sophistication of the proposed framework, we compared it with more than a dozen existing related attack methods. The effectiveness and efficiency of the proposed framework are verified from the extensive experimental results. The validity of the model structure is also validated by the ablation study.
Federated Semi-supervised Medical Image Segmentation Based on Asynchronous Transmission

Most existing medical image segmentation methods under federated learning use labeled data for training. But in practice, high-quality labeled data is scarce. Due to the specialized nature of medical images, manual labeling will waste a lot of manpower and resources. In addition, due to the private nature of medical images, datasets cannot be shared between institutions. Therefore, this paper proposes a semi-supervised medical image segmentation method based on asynchronous federated learning, which purposes to learn a federated model using a small amount of labelled data and a large amount of unlabeled data. First, to generate better pseudo-labels for locally unlabeled data, we set the local as the teacher model and the global as the student model to learn data information in the multi-agency. Then, the global student model is used to guide the generation of the local teacher model and the consistency regularization is performed. In addition, we use the student model to maintain the stability of the global model. Finally, we propose an asynchronous federated algorithm and local iterator mechanism to address the high load and latency problems on the central server caused by differences between client devices in synchronous transmission. Our experiments on two medical image segmentation tasks demonstrate that the method effectively validates the feasibility of federated semi-supervised segmentation and significantly improves model segmentation performance and communication efficiency.
PEA-U-Net: Parallel Embedded Attention for Liver and Tumor Segmentation in CT Volumes

The automatic segmentation of liver and tumor on CT is an urgent need in clinical practice. Currently existing high-precision algorithms based on deep learning have complex structures and configurations, or rely heavily on prior knowledge and tricks, which to some extent neglect the advantage of the model structure itself. Therefore, designing a simple end-to-end, highly universal, easy-to-deploy model that maintains high segmentation accuracy for liver and tumor remains a challenging task. We design two plug-and-play attention modules called BCSL and PEA, which focus on different functionalities to enhance information both on channel and spatial dimensions, as well as PEA-U-Net based on them. Experiments show that BCSL reduces the number of parameters without harming the performance, and the PEA module significantly outperforms other SOTA attention modules in segmenting tumors. Our proposed attention modules and network outperform other typical semantic segmentation networks in various metrics, which also reveals the versatility and huge potential of attention modules for other segmentation tasks.
Optimizing Cardiac Surgery Risk Prediction: An Machine Learning Approach with Counterfactual Explanations

Postoperative complications after cardiac surgery can be severe and even fatal, making it a high-risk procedure. Predicting surgical risk can guide the effective formulation of treatment plans for high-risk cardiac surgery, thereby reducing the risk of postoperative complications, which has attracted widespread attention from cardiac surgeons. The most commonly used method, EuroSCORE, has the problems of low prediction accuracy and weak targeting for postoperative complications. In this paper, we developed a machine learning (ML) model for predicting adverse outcomes (AO) after cardiac surgery with high accuracy and demonstrated the clinical interpretability of the model with counterfactual explanation (CE) based explainable artificial intelligence (XAI). A total of 2324 patients who had undergone cardiac surgeries with cardiopulmonary bypass support in a single center were included in this study, were divided into two groups as non-AO (n = 2148) and AO (n = 176). Our ML prediction model showed the best prediction performance using perioperative data (AUC = 0.769) when compared with models of EuroSCORE (AUC = 0.663) and EuroSCORE covariates (AUC = 0.710). CE method applied to the ML model showed how operation duration, ASA class, BMI, Lac entering ICU and PLT value increase the risk of adverse outcomes following surgery. In addition, sufficiency and necessity metrics was used to provide CE with a better explanation of feature importance. It has been proven that machine learning models have shown hope in improving the risk assessment of adverse outcomes after cardiac surgery, and counterfactual explanations methods provide more detailed and practical explanations, which are more useful for medical professionals.
DU-DANet: Efficient 3D Automatic Brain Tumor Segmentation Based on Dual Attention

Automatic segmentation of brain tumors using multimodal magnetic resonance imaging (MRI) has significant potential for clinical disease assessment. However, how to obtain more detailed features of brain tumors from brain images to further improve the accuracy of segmentation is still a critical issue. This paper proposes a new 3D multimodal brain tumor segmentation model with dual attention (DU-DANet), which uses two main modules of down-sampling with 1D attention convolutional (1D-DAC) and up-sampling with spatial and channel attention (USCA). 1D-DAC can preserve more low-level brain tumor features while reducing the resolution of brain images. USCA can fully fuse low-level feature maps with high-level ones to extract richer contextual information. In addition, USCA combines attention gates (AGs) and leverages channel relationships to effectively suppress irrelevant information, resulting in improved extraction of brain tumor features. Experiment results demonstrate that DU-DANet outperforms other state-of-the-art methods in both whole tumor and tumor core segmentation. Overall, the proposed DU-DANet model with USCA and 1D-DAC modules provides an efficient and accurate solution for automatic brain tumor segmentation, which has a certain potential for practical clinical applications.
BIJE: A Joint Extraction Model for Biomedical Information Extraction

The basic tasks of information extraction in biomedical text include named entity recognition and relation extraction. However, biomedical texts always contain a large number of sentences with complex structures, such as entity nesting and relation overlapping, which pose significant challenges to existing joint models. To address these issues, we propose a three-stage joint extraction model called BIJE, which uses an entity matrix-based strategy called BioPointer to solve the entity nesting problem, and the entity-relation exhaustive algorithm to tackle relation overlapping problem. Experimental results demonstrate that BIJE outperforms other methods on multiple biomedical datasets and prove that BIJE is a reliable framework for the information extraction from biomedical texts.
Patient Mortality Prediction Based on Two-Layer Attention Neural Network

With the development of medical informatization, electronic medical records are important in hospital information systems, and their use for patient mortality prediction can contribute to further improvement of clinical auxiliary diagnosis decision-making systems. Existing models for mortality risk prediction achieve good performance; however, data utilisation is limited predominantly to a single aspect. In this study, a scalable two-layer attention mechanism neural network is proposed to predict patient mortality by focusing on the patient diagnostic code, drug code, surgical code, and global condition. The first layer of the attention network uses three independent long short-term memory networks to learn the attention features of diagnosis, medication, and surgery of a patient single admission, and output three feature matrices. The feature matrix output of the first layer of the attention network is spliced and input for use, and the second layer of the attention network uses the transformer encoder. Finally, a fully connected layer is used to obtain the mortality prediction of the patient. The feasibility of the model is demonstrated by comparison with baseline methods and ablation experiments. In conclusion, the proposed model can comprehensively consider the information of patient diagnosis, medication, and surgery, and has good practicability and scalability.
Medical Image Segmentation Based on Federated Distillation Optimization Learning on Non-IID Data

Federated learning promises to alleviate this problem of low labelled data in medical image segmentation while protecting the privacy and security of the data. However, medical image segmentation under federated learning also has many problems, such as how to achieve high-precision segmentation using federated models in the presence of data imbalance, whether the communication efficiency in the federated process can be effectively improved, and how to effectively solve the model gradient explosion in federated distillation. Based on the above difficulties, this paper proposes a new optimization algorithm for federated distillation. First, we design a small-scale network model in the communication between the client and the central server to reduce the communication overhead; then, we design a distillation method to keep the local model stable. Finally, we add a coordinator for the central server before aggregation and introduce a model filtering mechanism to effectively filter and evaluate the client model parameters and weights to keep the global model optimization, while preventing the gradient explosion problem under malicious or extreme models and improving the accuracy of target domain segmentation. We conducted experiments on two medical image segmentation tasks and demonstrated that our approach achieves effective results on non-IID data, where the average DICE coefficient can reach 82.79% while the communication overhead is reduced by a factor of 16.
Cost Effective Approach for the Recertification of Open-Water Intervention Riser Systems in Nigeria using Asset Integrity Management Tools

Abstract This paper details the risk-based approach to optimize the recertification cost of Open-water intervention riser systems (OWIRS) in Nigeria. The recertification of Open-water intervention riser systems (OWIRS) is a conservative maintenance program that occurs every fifth year according to recommended practice and client requirements (1) (2) (3). Reliability and Integrity assurance are critical during any intervention activity because a runaway failure effect could escalate to an unwanted event, potentially impacting HSE, cost, and even reputation (4). The recertification of the Open-water intervention riser systems (OWIRS) has always been a capital-intensive activity due to the conventional approach deployed by various organizations resulting in a long period of asset unavailability and high cost. Although some notable recommended practices (1) (3) provide guidance but the approach for execution remains with the stakeholders. Due to the current Russia-Ukraine War and continuous drive to stay agile and lean for the removal of wastages, this paper has reviewed the cost element of typical recertification, adopted the hybrid technique comprising Failure Mode, Effect and Criticality Analysis (FMECA), and Reliability Centered Maintenance (RCM) decision tree (5) to develop a cost-effective approach to recertification for OWIRS. The results showed that specific and value-focused scopes driven through the aggregation of maintenance strategies are efficient. Engrafting an established and critical recertification maintenance scope into existing maintenance programs on Computerized Maintenance Management System (CMMS) that is driven more by a condition-based approach, condition monitoring and proactive tasks from lessons learned from field non-conformances reports and corrective actions provided a significant reduction to waste generated during the planning and execution of recertification project. The integration of recertification as part of the maintenance program is a pathway to saving great cost values of recertification whilst ensuring asset availability and Integrity (4).
Application of Blockchain Technology in the African Energy Industry: Use Cases, Limitations and Solutions

Abstract This paper explores the potential application of blockchain technology in the African energy industry, in the process, highlighting various use cases, identifying limitations, and proffering solutions to these limitations. The African energy industry is plagued by a plethora of problems such as a lack of access to reliable energy, the absence of transparency and trust, and limited access to financing amongst others. Blockchain technology can be employed to address these challenges by providing secure and transparent platforms for energy transactions, enabling peer-to-peer energy trading, and facilitating access to financing for both renewable and fossil energy projects. On the other hand, the implementation of blockchain technology in the energy industry faces several limitations which include a lack of regulatory clarity or a defined framework, inadequate infrastructure, and a lack of technical know-how. To address these limiting factors, this paper proffers several solutions such as the development of a clearly defined regulatory framework to enable the implementation of blockchain technology, the creation of industry standards to ensure interoperability and uniformity, and the promotion of education and training programs to build technical expertise. This paper also highlights existing case studies of blockchain-based energy projects in Africa to demonstrate the potential of blockchain technology in the energy industry. The paper concludes that while blockchain technology has the potential to revolutionize the African energy industry, early adoption is key and the importance of addressing existing limitations cannot be overemphasized.