Reviewerly/exHarmony huggingface dataset:
A two-stage federated optimization algorithm for privacy computing in Internet of Things

With the advent of the Internet of things (IoT) era, federated learning plays an important role in breaking through traditional data barriers and effectively realizing data privacy and security in the process of sharing. However, the demand of the practical problems makes the algorithm still have great challenges in effectively balancing various factors, such as privacy security, accuracy, computing efficiency and so on. To challenge this problem, a two-stage federated optimization algorithm based on robust and multitasking learning is designed. In optimization client local model stage, an adaptive weight assignment mechanism is adopted to guide robust learning based on multiple untrusted sources data, which aims to ensure the credibility and robustness of the client model and obtain a reliable client model. To address the information leakage problem during the server–client global model optimization stage, a privacy patch layer is added to the client local model in multitask learning and maintain its privacy parameters stored on the client model during the global model parameter aggregation process, which aims to meet the personalized requirements and performance requirements of protecting the model privacy. To effectively measure the performance of our algorithm, two extensive experiments are carried out to verify the robustness and accuracy of model under different datasets, respectively. In addition, simulation results show that our algorithm successfully suppresses the impact of corrupted or irrelevant sources on performance, and its performance is better than the other two robust distributed learning baseline methods in the client local model optimization stage. At the same time, our algorithm achieves better accuracy performance than other advanced personalized optimization algorithms in the server–client global model optimization stage. Finally, it achieves a good balance between robustness, computational efficiency and model privacy protection.
Feature selection algorithm based on binary BAT algorithm and optimum path forest classifier for breast cancer detection using both echographic and elastographic mode ultrasound images

Breast cancer is one of the fatal diseases among women. Every year, its incidence and mortality rate increase globally. Mammography and sonography are widely used in breast cancer detection. Because mammography misses many cancers and shows false negatives in the denser tissues, sonography is preferred to give some extra information in addition to that available from mammography.To improve the performance of breast cancer detection by reducing false positives.The local binary pattern (LBP) texture features must be extracted from ultrasound elastographic and echographic images of the same patients and then fused to form a single feature vector.Local Binary Pattern (LBP) texture features of elastographic and echographic images are extracted, and reduced individually through a hybrid feature selection technique based on binary BAT algorithm (BBA) and optimum path forest (OPF) classifier and then fused serially. Finally, the support vector machine classifier is used to classify the final fused feature set.Various relevant performance metrics such as accuracy, sensitivity, specificity, discriminant power, Mathews correlation coefficient (MCC), F1 score, and Kappa were used to analyze the classification results.The use of LBP feature produces 93.2% accuracy, 94.4% sensitivity, 92.3% specificity, 89.5% precision value, 91.88% F1 score, 93.34% balanced classification rate, and Mathews correlation coefficient of 0.861. The performance was compared with gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM), and LAWs features, and showed that LBP outperformed.Because the specificity is better, this method could be useful for detecting breast cancer with minimum false negatives.
Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning

Convolutional neural networks (CNNs) have become a popular choice for various image classification applications. However, the multi-layer perceptron mixer (MLP-Mixer) architecture has been proposed as a promising alternative, particularly for large datasets. Despite its advantages in handling large datasets and models, MLP-Mixer models have limitations when dealing with small datasets. This study aimed to quantify and evaluate the uncertainty associated with MLP-Mixer models for small datasets using Bayesian deep learning (BDL) methods to quantify uncertainty and compare the results to existing CNN models. In particular, we examined the use of variational inference and Monte Carlo dropout methods. The results indicated that BDL can improve the performance of MLP-Mixer models by 9.2 to 17.4% in term of accuracy across different mixer models. On the other hand, the results suggest that CNN models tend to have limited improvement or even decreased performance in some cases when using BDL. These findings suggest that BDL is a promising approach to improve the performance of MLP-Mixer models, especially for small datasets.
MBTI Personality Prediction Using Machine Learning and SMOTE for Balancing Data Based on Statement Sentences

The rise of social media as a platform for self-expression and self-understanding has led to increased interest in using the Myers–Briggs Type Indicator (MBTI) to explore human personalities. Despite this, there needs to be more research on how other word-embedding techniques, machine learning algorithms, and imbalanced data-handling techniques can improve the results of MBTI personality-type predictions. Our research aimed to investigate the efficacy of these techniques by utilizing the Word2Vec model to obtain a vector representation of words in the corpus data. We implemented several machine learning approaches, including logistic regression, linear support vector classification, stochastic gradient descent, random forest, the extreme gradient boosting classifier, and the cat boosting classifier. In addition, we used the synthetic minority oversampling technique (SMOTE) to address the issue of imbalanced data. The results showed that our approach could achieve a relatively high F1 score (between 0.7383 and 0.8282), depending on the chosen model for predicting and classifying MBTI personality. Furthermore, we found that using SMOTE could improve the selected models’ performance (F1 score between 0.7553 and 0.8337), proving that the machine learning approach integrated with Word2Vec and SMOTE could predict and classify MBTI personality well, thus enhancing the understanding of MBTI.
Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies

Abstract Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever‐growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a “roadmap” detailing the current research trends in the field of network visualization. We design our contribution as a meta‐survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.
Joint Segmentation and Differential Diagnosis of Thyroid Nodule in Contrast-Enhanced Ultrasound Images

Microvascular perfusion can be observed in real time with contrast-enhanced ultrasound (CEUS), which is a novel ultrasound technology for visualizing the dynamic patterns of parenchymal perfusion. Automatic lesion segmentation and differential diagnosis of malignant and benign based on CEUS are crucial but challenging tasks for computer-aided diagnosis of thyroid nodule.To tackle these two formidable challenges concurrently, we provide Trans-CEUS, a spatial-temporal transformer-based CEUS analysis model to finish the joint learning of these two challenging tasks. Specifically, the dynamic swin-transformer encoder and multi-level feature collaborative learning are combined into U-net for achieving accurate segmentation of lesions with ambiguous boundary from CEUS. In addition, variant transformer-based global spatial-temporal fusion is proposed to obtain long-distance enhancement perfusion of dynamic CEUS for promoting differential diagnosis.Empirical results of clinical data showed that our Trans-CEUS model achieved not only a good lesion segmentation result with a high Dice similarity coefficient of 82.41%, but also superior diagnostic accuracy of 86.59%. Conclusion & significance: This research is novel since it is the first to incorporate the transformer into CEUS analysis, and it shows promising results on dynamic CEUS datasets for both segmentation and diagnosis tasks of the thyroid nodule.
Globalisation and Inclusive Growth in Africa: The Role of Institutional Quality

This study examines the relationship between globalisation and inclusive growth by considering the modulating role of institutional quality. To achieve our broad objective, we use data from 45 African economies over 1996–2018 to determine the panel cointegration and cointegrating regression association between inclusive growth, globalisation and institutional quality. To determine a suitable estimation technique for the empirical analysis, several pre-estimation tests were conducted. After confirming the existence of cointegration and slope heterogeneity, we adapted the long-run panel cointegrating methods—the fully modified ordinary least squares and dynamic ordinary least squares estimations. The results from both show that aggregate globalisation and its various dimensions have positive and significant effects on inclusive growth. Besides the direct positive impact on inclusive growth, globalisation has indirect positive and significant impact on inclusive growth through institutional quality. Finally, some policy implications are highlighted. JEL Codes: E02, F62, F63, O15, O43
Attention guided neural ODE network for breast tumor segmentation in medical images

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer. Doctors make diagnoses and further instructions through the sizes, shapes and textures of the breast masses showed on medical images, so automatic tumor segmentation via deep neural networks can to some extent assist doctors. Compared to some challenges which the popular deep neural networks have faced, such as large amounts of parameters, lack of interpretability, overfitting problem, etc., we propose a segmentation network named Att-U-Node which uses attention modules to guide a neural ODE-based framework, trying to alleviate the problems mentioned above. Specifically, the network uses ODE blocks to make up an encoder-decoder structure, feature modeling by neural ODE is completed at each level. Besides, we propose to use an attention module to calculate the coefficient and generate a much refined attention feature for skip connection. Three public available breast ultrasound image datasets (i.e. BUSI, BUS and OASBUD) and a private breast DCE-MRI dataset are used to assess the efficiency of the proposed model, besides, we upgrade the model to 3D for tumor segmentation with the data selected from Public QIN Breast DCE-MRI. The experiments show that the proposed model achieves competitive results compared with the related methods while mitigates the common problems of deep neural networks.
Lightweight multilayer interactive attention network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to automatically identify the sentiment polarity of specific aspect words in a given sentence or document. Existing studies have recognised the value of interactive learning in ABSA and have developed various methods to precisely model aspect words and their contexts through interactive learning. However, these methods mostly take a shallow interactive way to model aspect words and their contexts, which may lead to the lack of complex sentiment information. To solve this issue, we propose a Lightweight Multilayer Interactive Attention Network (LMIAN) for ABSA. Specifically, we first employ a pre-trained language model to initialise word embedding vectors. Second, an interactive computational layer is designed to build correlations between aspect words and their contexts. Such correlation degree is calculated by multiple computational layers with neural attention models. Third, we use a parameter-sharing strategy among the computational layers. This allows the model to learn complex sentiment features with lower memory costs. Finally, LMIAN conducts instance validation on six publicly available sentiment analysis datasets. Extensive experiments show that LMIAN performs better than other advanced methods with relatively low memory consumption.
State of the art in financial statement fraud detection: A systematic review

Over the past few decades, fraud has been increasingly prevalent, with large businesses like Satyam, Enron, and WorldCom making headlines for their deceptive financial reporting practices. In this research, we conducted a systematic review and bibliometric analysis of the literature concerning fraud detection in financial statements. Following a bibliometric analysis, we identified the leading researchers, publications, sources, countries, and collaboration patterns in financial statement fraud detection. Our systematic review covered the following topics: the data analytics tools used, databases used to identify fraudulent firms, the design of control group samples (non-fraudulent firms), the critical dimension reduction tools used, techniques adopted to address data rarity (imbalanced data), explanatory variables used in the model, theoretical framework supporting the fraud indicators, optimization techniques used, the use of evaluation metrics, and significant findings. The systematic review followed the approach provided by Tranfield et al. (2003), and the bibliometric analysis was conducted using the VOSviewer. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), 2020 reporting criteria were followed for reporting the systematic review's findings. We provide a brief overview of the existing literature, drawing both conclusions and recommendations for directions in which additional study is warranted. Our results provide valuable information that can be used by future academics, auditors, enforcement agencies, and regulators as they work to create the most effective fraud detection algorithms possible.
Application of Wearable Gloves for Assisted Learning of Sign Language Using Artificial Neural Networks

This study proposes the design and application of wearable gloves that can recognize sign language expressions from input images via long short-term memory (LSTM) network models and can learn sign language through finger movement generation and vibration motor feedback. It is difficult for nondisabled people who do not know sign language to express sign language accurately. Therefore, we suggest the use of wearable gloves for sign language education to help nondisabled people learn and accurately express sign language. The wearable glove consists of a direct current motor, a link (finger exoskeleton) that can generate finger movements, and a flexible sensor that recognizes the degree of finger bending. When the coordinates of the hand move in the input image, the sign language motion is fed back through the vibration motor attached to the wrist. The proposed wearable glove can learn 20 Korean sign language words, and the data used for learning are configured to represent the joint coordinates and joint angles of both the hands and body for these 20 sign language words. Prototypes were produced based on the design, and it was confirmed that the angle of each finger could be adjusted. Through experiments, a sign language recognition model was selected, and the validity of the proposed method was confirmed by comparing the generated learning results with the data sequence. Finally, we compared and verified the accuracy and learning loss using a recurrent neural network and confirmed that the test results of the LSTM model showed an accuracy of 85%.
A Multi-Attention Approach for Person Re-Identification Using Deep Learning

Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often involve the attention module, have gained remarkable success. Moreover, people's traits are mostly similar, which makes distinguishing between them complicated. This paper presents a novel approach for person Re-ID, by introducing a multi-part feature network, that combines the position attention module (PAM) and the efficient channel attention (ECA). The goal is to enhance the accuracy and robustness of person Re-ID methods through the use of attention mechanisms. The proposed multi-part feature network employs the PAM to extract robust and discriminative features by utilizing channel, spatial, and temporal context information. The PAM learns the spatial interdependencies of features and extracts a greater variety of contextual information from local elements, hence enhancing their capacity for representation. The ECA captures local cross-channel interaction and reduces the model's complexity, while maintaining accuracy. Inclusive experiments were executed on three publicly available person Re-ID datasets: Market-1501, DukeMTMC, and CUHK-03. The outcomes reveal that the suggested method outperforms existing state-of-the-art methods, and the rank-1 accuracy can achieve 95.93%, 89.77%, and 73.21% in trials on the public datasets Market-1501, DukeMTMC-reID, and CUHK03, respectively, and can reach 96.41%, 94.08%, and 91.21% after re-ranking. The proposed method demonstrates a high generalization capability and improves both quantitative and qualitative performance. Finally, the proposed multi-part feature network, with the combination of PAM and ECA, offers a promising solution for person Re-ID, by combining the benefits of temporal, spatial, and channel information. The results of this study evidence the effectiveness and potential of the suggested method for person Re-ID in computer vision applications.
A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning Techniques

Abstract Multiple Choice Questions (MCQs) are a popular assessment method because they enable automated evaluation, flexible administration and use with huge groups. Despite these benefits, the manual construction of MCQs is challenging, time-consuming and error-prone. This is because each MCQ is comprised of a question called the "stem", a correct option called the "key" along with alternative options called "distractors" whose construction demands expertise from the MCQ developers. In addition, there are different kinds of MCQs such as Wh-type, Fill-in-the-blank, Odd one out, and many more needed to assess understanding at different cognitive levels. Automatic Question Generation (AQG) for developing heterogeneous MCQ stems has generally followed two approaches: semantics-based and machine-learning-based. Questions generated via AQG techniques can be utilized only if they are grammatically correct. Semantics-based techniques have been able to generate a range of different types of grammatically correct MCQs but require the semantics to be specified. In contrast, most machine-learning approaches have been primarily able to generate only grammatically correct Fill-in-the-blank/Cloze by reusing the original text. This paper describes a technique for combining semantic-based and machine-learning-based techniques to generate grammatically correct MCQ stems of various types for a technical domain. Expert evaluation of the resultant MCQ stems demonstrated that they were promising in terms of their usefulness and grammatical correctness.
Pixel-wise content attention learning for single-image deraining of autonomous vehicles

Improving the performance of autonomous vehicles in adverse weather conditions is vital for the commercialization of such automated systems. Existing synthetic datasets for developing rain-tolerant vision are of limited value. To address this deficiency, a closed environment capable of simulating different degrees of rainfall is constructed. And a new Closed Field Rain dataset is collected in 36 testing cycles. Inspired by the idea that human can infer the content of rainy images directly without removing the raindrops. A new single-image deraining method is proposed, that does not require ground truth images. This method incorporates an image content estimation module applied to predict the scene content representation, and a pixel-wise content attention block used to evaluate the significance of each pixel. After that, an encoder-decoder network is applied to complete the image. On the other hand, it is almost impossible to obtain the ground truth of rainy images because of the dynamic characteristics of real traffic environment. Thus, the model is trained by employing PatchGAN, using a patch-based loss. Using common no-reference and feature point metrics as performance indicators, this paper conducts a comprehensive evaluation on both synthetic and real-world datasets including Closed Field Rain dataset. Results show the effectiveness of our model quantitatively and qualitatively.