Reviewerly/exHarmony huggingface dataset:
Feature importance in machine learning models: A fuzzy information fusion approach

With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Although post-training feature importance approaches assist this interpretation, there is an overall lack of consensus regarding how feature importance should be quantified, making explanations of model predictions unreliable. In addition, many of these explanations depend on the specific machine learning approach employed and on the subset of data used when calculating feature importance. A possible solution to improve the reliability of explanations is to combine results from multiple feature importance quantifiers from different machine learning approaches coupled with re-sampling. Current state-of-the-art ensemble feature importance fusion uses crisp techniques to fuse results from different approaches. There is, however, significant loss of information as these approaches are not context-aware and reduce several quantifiers to a single crisp output. More importantly, their representation of “importance” as coefficients may be difficult to comprehend by end-users and decision makers. Here we show how the use of fuzzy data fusion methods can overcome some of the important limitations of crisp fusion methods by making the importance of features easily understandable.
Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation

Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges.
Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.
Topological order and entanglement dynamics in the measurement-only XZZX quantum code

We examine the dynamics of a $(1+1)$-dimensional measurement-only circuit defined by the stabilizers of the [[5,1,3]] quantum error correcting code interrupted by single-qubit Pauli measurements. The code corrects arbitrary single-qubit errors and it stabilizes an area law entangled state with a $D_2 = \mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry protected topological (SPT) order, as well as a symmetry breaking (SB) order from a two-fold bulk degeneracy. The Pauli measurements break the topological order and induce a phase transition into a trivial area law phase. Allowing more than one type of Pauli measurement increases the measurement-induced frustration, and the SPT and SB order can be broken either simultaneously or separately at nonzero measurement rate. This yields a rich phase diagram and unanticipated critical behavior at the phase transitions. Although the correlation length exponent $\nu=\tfrac43$ and the dynamical critical exponent $z=1$ are consistent with bond percolation, the prefactor of the logarithmic entanglement growth may take non-integer multiples of the percolation value. Remarkably, we identify a robust transient scaling regime for the purification dynamics of $L$ qubits. It reveals a modified dynamical critical exponent $z^*\neq z$, which is observable up to times $t\sim L^{z^*}$ and is reminiscent of the relaxation of critical systems into a prethermal state.
Uncertainty-aware pseudo-label and consistency for semi-supervised medical image segmentation

In medical image segmentation tasks, fully-supervised learning has been a huge success by using abundant labeled data. However, it is time-consuming and expensive for technicians to label medical images. In this paper, we propose a novel framework for semi-supervised medical image segmentation, named Uncertainty-aware Pseudo-label and Consistency. Our framework is made up of the student–teacher models. The supervised loss on labeled data and the consistency loss on both labeled and unlabeled data are weighted and combined to optimize the models. Our method combines the recent state-of-the-art semi-supervised methods, which are consistency regularization and pseudo-labeling. More importantly, we calculate the Kullback–Leibler variance between the student model’s prediction and the teacher model’s prediction as uncertainty estimation, and directly use the uncertainty to rectify the learning of noisy pseudo-labels, instead of setting a fixed threshold to filter the pseudo-labels. Experiments on the Left Atrium dataset show that our method can efficiently utilize unlabeled data to achieve high performance and outperform other state-of-the-art semi-supervised methods. In addition, we have also analyzed its difference from conventional methods of consistency regularization and pseudo-labeling in semi-supervised medical image segmentation. Code is available in https://github.com/GXU-GMU-MICCAI/UPC-Pytorch.
HyVADRF: Hybrid VADER–Random Forest and GWO for Bitcoin Tweet Sentiment Analysis

In recent years, Bitcoin and other cryptocurrencies have been increasingly considered an investment option for emerging markets. However, its erratic behavior has discouraged some potential investors. To get insights into its behavior and price fluctuation, past studies have discovered the correlation between Twitter sentiments and Bitcoin behavior. Most of them have focused exclusively on their relationships, instead of the Twitter sentiment analysis itself. Finding the most suitable classification algorithms for sentiment analysis for this kind of data is challenging. For enormous data of Twitter, unlabeled data can be time-consuming and expensive for the supervised sentiment analysis approach, which has been studied to be superior to unsupervised ones. As such, we propose HyVADRF: Hybrid VADER – Random Forest and Grey Wolf Optimizer Model. Semantic and rule-based VADER was used to calculate polarity scores and classify sentiments, which overcame the weakness of manual labeling, while Random Forest was utilized as its supervised classifier. Furthermore, considering Twitter’s massive size, we collected over 3.6 million tweets and analyzed various dataset sizes as these are related to the model’s learning process. Lastly, Grey Wolf Optimizer parameter tuning was conducted to optimize the classifier’s performance. The results show that 1) HyVADRF Model returned an accuracy of 75.29 %, precision of 70.22%, recall of 87.70%, and F1-score of 78%. 2) The most ideal percentage of dataset size is 90% of the total collected tweets (n=1,249,060). 3) With standard deviations of 0.0008 for accuracy and F1-score and 0.0011 for precision and recall. Hence, HyVADRF Model consistently delivers stable results.
A Survey of Information Extraction Based on Deep Learning

As a core task and an important link in the fields of natural language understanding and information retrieval, information extraction (IE) can structure and semanticize unstructured multi-modal information. In recent years, deep learning (DL) has attracted considerable research attention to IE tasks. Deep learning-based entity relation extraction techniques have gradually surpassed traditional feature- and kernel-function-based methods in terms of the depth of feature extraction and model accuracy. In this paper, we explain the basic concepts of IE and DL, primarily expounding on the research progress and achievements of DL technologies in the field of IE. At the level of IE tasks, it is expounded from entity relationship extraction, event extraction, and multi-modal information extraction three aspects, and creates a comparative analysis of various extraction techniques. We also summarize the prospects and development trends in DL in the field of IE as well as difficulties requiring further study. It is believed that research can be carried out in the direction of multi-model and multi-task joint extraction, information extraction based on knowledge enhancement, and information fusion based on multi-modal at the method level. At the model level, further research should be carried out in the aspects of strengthening theoretical research, model lightweight, and improving model generalization ability.
AI-powered public surveillance systems: why we (might) need them and how we want them

In this article, we address the introduction of AI-powered surveillance systems in our society by looking at the deployment of real-time facial recognition technologies (FRT) in public spaces and public health surveillance technologies, in particular contact tracing applications. Both cases of surveillance technologies assist public authorities in the enforcement of the law by allowing the tracking of individual movements and extrapolating results towards monitoring and predicting social behavior. Therefore, they are considered as potentially useful tools in response to societal crises, such as those generated by crime and health related pandemics. To approach the assessment of the potentials and threats of such tools, we offer a framework with three dimensions. A function dimension, examines the type, quality and quantity of data the system needs to employ to work effectively.The consent dimension considers the user's right to be informed about and reject the use of surveillance, questioning whether consent is achievable and whether the user can decide fully autonomously/independently. Finally, a societal dimension that frames vulnerabilities and the impacts of the increased empowerment of established political regimes through new means to control populations based on data surveillance. Our analysis framework can assist public authorities in their decisions on how to design and deploy public surveillance tools in a way that enables compliance with the law while highlighting individual and societal tradeoffs.
Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools

Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum Computer platforms, Circuit Simulator, Open-source Tools- Cirq, TensorFlow Quantum, ProjectQ etc. that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges, and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
Code Smell Detection Using Ensemble Machine Learning Algorithms

Code smells are the result of not following software engineering principles during software development, especially in the design and coding phase. It leads to low maintainability. To evaluate the quality of software and its maintainability, code smell detection can be helpful. Many machine learning algorithms are being used to detect code smells. In this study, we applied five ensemble machine learning and two deep learning algorithms to detect code smells. Four code smell datasets were analyzed: the Data class, the God class, the Feature-envy, and the Long-method datasets. In previous works, machine learning and stacking ensemble learning algorithms were applied to this dataset and the results found were acceptable, but there is scope of improvement. A class balancing technique (SMOTE) was applied to handle the class imbalance problem in the datasets. The Chi-square feature extraction technique was applied to select the more relevant features in each dataset. All five algorithms obtained the highest accuracy—100% for the Long-method dataset with the different selected sets of metrics, and the poorest accuracy, 91.45%, was achieved by the Max voting method for the Feature-envy dataset for the selected twelve sets of metrics.
Inverse free reduced universum twin support vector machine for imbalanced data classification

Imbalanced datasets are prominent in real-world problems. In such problems, the data samples in one class are significantly higher than in the other classes, even though the other classes might be more important. The standard classification algorithms may classify all the data into the majority class, and this is a significant drawback of most standard learning algorithms, so imbalanced datasets need to be handled carefully. One of the traditional algorithms, twin support vector machines (TSVM), performed well on balanced data classification but poorly on imbalanced datasets classification. In order to improve the TSVM algorithm's classification ability for imbalanced datasets, recently, driven by the universum twin support vector machine (UTSVM), a reduced universum twin support vector machine for class imbalance learning (RUTSVM) was proposed. The dual problem and finding classifiers involve matrix inverse computation, which is one of RUTSVM's key drawbacks. In this paper, we improve the RUTSVM and propose an improved reduced universum twin support vector machine for class imbalance learning (IRUTSVM). We offer alternative Lagrangian functions to tackle the primal problems of RUTSVM in the suggested IRUTSVM approach by inserting one of the terms in the objective function into the constraints. As a result, we obtain new dual formulation for each optimization problem so that we need not compute inverse matrices neither in the training process nor in finding the classifiers. Moreover, the smaller size of the rectangular kernel matrices is used to reduce the computational time. Extensive testing is carried out on a variety of synthetic and real-world imbalanced datasets, and the findings show that the IRUTSVM algorithm outperforms the TSVM, UTSVM, and RUTSVM algorithms in terms of generalization performance.
Towards open-set text recognition via label-to-prototype learning

• Formulating a new open-set task requires spotting and cognizing novel characters. • Proposing a new framework that handles novel characters without retraining. • Proposing a fast rectification technique for text recognition. Scene text recognition is a popular research topic which is also extensively utilized in the industry. Although many methods have achieved satisfactory performance for the close-set text recognition challenges, these methods lose feasibility in open-set scenarios, where collecting data or retraining models for novel characters could yield a high cost. For example, annotating samples for foreign languages can be expensive, whereas retraining the model each time when a “novel” character is discovered from historical documents costs both time and resources. In this paper, we introduce and formulate a new open-set text recognition task which demands the capability to spot and recognize novel characters without retraining. A label-to-prototype learning framework is also proposed as a baseline for the new task. Specifically, the framework introduces a generalizable label-to-prototype mapping function to build prototypes (class centers) for both seen and unseen classes. An open-set predictor is then utilized to recognize or reject samples according to the prototypes. The implementation of rejection capability over out-of-set characters allows automatic spotting of unknown characters in the incoming data stream. Extensive experiments show that our method achieves promising performance on a variety of zero-shot, close-set, and open-set text recognition datasets.
Human capital and organizational performance: A moderation study through innovative leadership

The present study investigates the impact of human capital capacity, human capital knowledge, and human capital skills on organisational performance in the hospitality industry. This study also investigates the moderating role of innovative leadership in the relationship of human capital capacity, human capital knowledge, and human capital skills with organisational performance. Data was collected through survey questionnaires from 356 managers working in small and middle-level hotels located in the four districts of Saudi Arabia. The present study used convenience sampling, and the data analysis method was partial least square structural equation modelling. Results of this study demonstrate that human capital capacity, human capital knowledge and human capital skills have a significant positive relationship with organizational performance. Results also confirmed a moderation effect of innovative leadership between human capital knowledge and organizational performance. However, the moderation effect of innovative leadership between human capital capacity and human capital skills with organizational performance was not confirmed. Finally, the theoretical contribution, practical implications and future commendations are also discussed.
Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilizing the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimize dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes.
Blockchain-of-blockchains: An interoperable blockchain platform for ensuring IoT data integrity in smart city

We propose a hierarchical blockchain-based platform for ensuring the integrity of smart city Internet-of-Things (IoT) data and blockchain interoperability in this paper. The well-defined structural hierarchy of managing organizations in a smart city face several data management issues. The integration of emerging technologies, such as (IoT), has introduced more complexity to data maintenance in the smart city. At present, cloud-based information systems have become the norm for managing IoT data. Due to the centralized administrative control, the cloud-based information management is untrusted. Hence, it fails to guarantee the integrity of IoT data and traceability of operations to achieve transparency within the smart city organizations. Adoption of blockchain-based information systems can be viable solution to address these challenges. However, it introduces a new challenge of interoperability of transactions among different smart city organizations. In this paper, we introduce a decentralized hierarchy of blockchains, named Blockchain-of-Blockchains (BoBs), for ensuring data integrity and blockchain interoperability at the same time. We implement the proposed concept using Hyperledger Fabric and Ethermint as a proof-of-concept for evaluating the performance of the proposed concept.