Reviewerly/exHarmony huggingface dataset:
Quantum Support Vector Machines for Aerodynamic Classification

Aerodynamics plays an important role in the aviation industry and aircraft design. Detecting and minimizing the phenomenon of flow separation from scattered pressure data on the airfoil is critical for ensuring stable and efficient aviation. However, given that it is challenging to understand the mechanics of flow-field separation, aerodynamic parameters are emphasized for the identification and control of flow separation. The mechanics of flow-field separation have been extensively investigated using traditional algorithms and machine learning methods such as support vector machine (SVM) models. Recently, growing interest in quantum computing and its applications in various research communities has shed light on the use of quantum techniques to solve aerodynamic problems. In this study, we applied qSVM, a quantum SVM algorithm based on the quantum annealing model, to identify flow separation and compared its performance to that of the widely used classical SVM. We demonstrated that our approach outperforms the classical SVM with an 11.1% increase in accuracy, from 0.818 to 0.909, for this binary classification task. We further developed a multiclass qSVM based on a one-against-all algorithm and applied it to the classification of multiple angles of attack on the wings, where its advantage over its classical multiclass counterparts was maintained with a 17.9% increase in accuracy, from 0.67 to 0.79. Our study demonstrates a useful quantum technique for classifying flow separation scenarios and may promote the investigation of quantum computing applications in fluid dynamics.
Food prices and poverty in Africa

Abstract This study investigated the effect of food prices on poverty in 37 sub‐Saharan African economies between 2001 and 2020. The study employed Prais‐Winsten regression, variations of the Fixed Effects model to account for conventional biases in econometric modeling, and the Method of Moment Quantile Regression with Fixed Effects to account for existing poverty levels. The findings revealed that a higher proportion of Africans will fall into poverty due to the increase in food prices. This result is consistent with the presence of endogeneity, cross‐sectional dependence, and unobservable heterogeneity. The study also reveals that the increase in food prices has a greater effect in sub‐Saharan African countries with lower levels of poverty as compared to those with high poverty levels, showing that rising food prices disproportionately hurt countries with low poverty levels, pushing more Africans below the poverty line in those countries. The study recommends the need for the implementation of supply‐side agricultural policies that enhance productivity in the sector, thereby boosting aggregate agricultural supply and precipitating a decline in food prices.
Use case modeling in a research setting of developing an innovative pilgrimage support system

Abstract With so much confusion around use case modeling, software developers may be reluctant to apply it. However, use cases are easy to apply and have great benefits even in research settings with nonprofessional software developers involved. We report on our experience on this within an innovative tourist information system developed in a research setting of an ongoing Horizon 2020 project named Promotion of Rural Museums and Heritage Sites in the Vicinity of European Pilgrimage Routes (rurAllure). The resulting use case model comprises forty use cases supported by class diagrams, use case diagrams, sequence diagrams, as well as by the domain model and dictionary. Three quarters of use cases have been implemented at least to some extent. Writing use cases provoked intense communication, which helped consolidate the system architecture. The use cases helped validate and better understand the GUI form wireframes designed beforehand and get quickly to a consistent implementation of the system that can be experimented with further in different ways. Finally, the forthcoming extensive system and user experience testing will be guided by the use case model. The paper also exposes the domain model and dictionary along with selected use cases and the hierarchy of user and system actors, which may be useful as such or as a stimulus for the development of other innovative tourist information systems.
A Notion of Feature Importance by Decorrelation and Detection of Trends by Random Forest Regression

In many studies, we want to determine the influence of certain features on a dependent variable. More specifically, we are interested in the strength of the influence – i.e., is the feature relevant? And, if so, how the feature influences the dependent variable. Recently, data-driven approaches such as random forest regression have found their way into applications (Boulesteix et al. 2012). These models allow researchers to directly derive measures of feature importance, which are a natural indicator of the strength of the influence. For the relevant features, the correlation or rank correlation between the feature and the dependent variable has typically been used to determine the nature of the influence. More recent methods, some of which can also measure interactions between features, are based on a modeling approach. In particular, when machine learning models are used, SHAP scores are a recent and prominent method to determine these trends (Lundberg et al. 2017). In this paper, we introduce a novel notion of feature importance based on the well-studied Gram-Schmidt decorrelation method. Furthermore, we propose two estimators for identifying trends in the data using random forest regression, the so-called absolute and relative traversal rate. We empirically compare the properties of our estimators with those of well-established estimators on a variety of synthetic and real-world datasets.
Certified polyhedral decompositions of collision-free configuration space

Understanding the geometry of collision-free configuration space (C-free) in the presence of Cartesian-space obstacles is an essential ingredient for collision-free motion planning. While it is possible to check for collisions at a point using standard algorithms, to date no practical method exists for computing C-free regions with rigorous certificates due to the complexity of mapping Cartesian-space obstacles through the kinematics. In this work, we present the first to our knowledge rigorous method for approximately decomposing a rational parametrization of C-free into certified polyhedral regions. Our method, called C-Iris (C-space Iterative Regional Inflation by Semidefinite programming), generates large, convex polytopes in a rational parameterization of the configuration space which are rigorously certified to be collision-free. Such regions have been shown to be useful for both optimization-based and randomized motion planning. Based on convex optimization, our method works in arbitrary dimensions, only makes assumptions about the convexity of the obstacles in the 3D Cartesian space, and is fast enough to scale to realistic problems in manipulation. We demonstrate our algorithm’s ability to fill a non-trivial amount of collision-free C-space in several 2-DOF examples where the C-space can be visualized, as well as the scalability of our algorithm on a 7-DOF KUKA iiwa, a 6-DOF UR3e, and 12-DOF bimanual manipulators. An implementation of our algorithm is open-sourced in Drake . We furthermore provide examples of our algorithm in interactive Python notebooks .
Skeleton-based Human Action Recognition in a Thermal Comfort Context

Thermal comfort optimization is key to ensuring the well-being of building occupants and promoting intelligent energy use in the built environment. In the current space of thermal comfort analysis, many techniques are used ranging from more intrusive wearable sensors and qualitative occupant surveys to less intrusive infrared thermal monitoring and human action recognition (HAR). However, as these methods increase surveillance of building occupants in often complex environments, privacy preservation and accurate analysis are essential for optimal thermal control. This paper focuses on uplifting the ability of skeleton-based HAR for use in thermal comfort-related action recognition as this method has been shown to have promising action recognition accuracy while maintaining user privacy. While we focus here on thermal comfort, our analysis and emphasis on the use and advancement of privacy-preserving technologies naturally extend to other systems (e.g., cyber-physical-social infrastructure systems) underpinned by human-centered, or "social," objectives and interactions. We benchmark several fundamental deep learning models in the skeleton-based HAR space and compare their performance on a new dataset of thermal comfort-related actions.
Semantic Web and blockchain technologies: Convergence, challenges and research trends

In recent years, on the one hand, we have witnessed the rise of blockchain technology, which has led to better transparency, traceability, and therefore, trustworthy exchange of digital assets among different actors. On the other hand, achieving trustworthy content exchange has been one of the primary objectives of the Semantic Web, part of the World Wide Web Consortium. Semantic Web and blockchain technologies are the fundamental building blocks of Web3 (the third version of the Internet), which aims to link data through a decentralized approach. Blockchain provides a decentralized and secure framework for users to safeguard their data and take control over their data and Web3 experiences. However, developing trustworthy decentralized applications (Dapps) is a challenge because many blockchain-based functionalities must be developed from scratch, and combined with data semantics to open new innovative opportunities. In this survey paper, we explore the cross-cutting domain of the Semantic Web and blockchain and identify the critical building blocks required to achieve trust in the Next-Generation Internet. The application domains that could benefit from these technologies are also investigated. We developed a deep analysis of the published literature between 2015 and 2023. We performed our analysis in different digital libraries (e.g., Elsevier, IEEE, ACM), and as a result of our research, we retrieved 137 papers, of which 97 were retrieved as relevant to include in the paper. Furthermore, we studied several aspects (e.g., network type, transactions per second) of existing blockchain platforms. Semantic Web and blockchain technologies can be used to realize a verification and certification process for data quality. Examples of mechanisms to achieve this are the Decentralized Identities of the Semantic Web or the various blockchain consensus protocols that help achieve decentralization and realize democratic principles. Therefore, Semantic Web and blockchain technologies should be combined to achieve trust in the highly decentralized, semantically complex, and dynamic environments needed to build smart applications of the future.
Adopting extended reality? A systematic review of manufacturing training and teaching applications

The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training.
Exploring the Potentials and Challenges of Cyber-Physical-Social Infrastructure Systems for Achieving Human-Centered Objectives

For decades, cyber-physical systems (CPSs) have transformed the design and management of infrastructure systems, improving their performance through the combination of sensing, computing, and control. However, while the existing CPS paradigm supports infrastructure system actuation to achieve desired economic objectives (e.g., efficiency, safety), it fails to account for possible input-output relationships between physical system actuation and human-centered (or "social") benefits and objectives. There are entirely unexplored social benefits to be derived from infrastructure systems that have yet to be scientifically understood and exploited. For instance, the impact of public space design and management on social interactions. To address this untapped potential, we first highlight the existing gaps and challenges to reimagining CPS theory capable of measuring, modeling, and actuating social objectives within physical infrastructure systems. We find that these challenges introduced by the integration of human-centered objectives necessitate an entirely new approach—as opposed to explicit extension of CPS—which we term cyber-physical-social infrastructure systems (CPSISs). Thus, we propose a high-level CPSIS framework to serve as a roadmap for advancements within this emerging field.
MIPA-ResGCN: a multi-input part attention enhanced residual graph convolutional framework for sign language recognition

Sign language (SL) is used as primary mode of communication by individuals who experience deafness and speech disorders. However, SL creates an inordinate communication barrier as most people are not acquainted with it. To solve this problem, many technological solutions using wearable devices, video, and depth cameras have been put forth. The ubiquitous nature of cameras in contemporary devices has resulted in the emergence of sign language recognition (SLR) using video sequence as a viable and unobtrusive substitute. Nonetheless, the utilization of SLR methods based on visual features, commonly known as appearance-based methods, presents notable computational complexities. In response to these challenges, this study introduces an accurate and computationally efficient pose-based approach for SLR. Our proposed approach comprises three key stages: pose extraction, handcrafted feature generation, and feature space mapping and recognition. Initially, an efficient off-the-shelf pose extraction algorithm is employed to extract pose information of various body parts of a subject captured in a video. Then, a multi-input stream has been generated using handcrafted features, i.e., joints, bone lengths, and bone angles. Finally, an efficient and lightweight residual graph convolutional network (ResGCN) along with a novel part attention mechanism, is proposed to encode body's spatial and temporal information in a compact feature space and recognize the signs performed. In addition to enabling effective learning during model training and offering cutting-edge accuracy, the proposed model significantly reduces computational complexity. Our proposed method is assessed on five challenging SL datasets, WLASL-100, WLASL-300, WLASL-1000, LSA-64, and MINDS-Libras, achieving state-of-the-art (SOTA) accuracies of 83.33 %, 72.90 %, 64.92 %, 100± 0 %, and 96.70± 1.07 %, respectively. Compared to previous approaches, we achieve superior performance while incurring a lower computational cost.
A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite’s ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate.
FastNER: Speeding up Inferences for Named Entity Recognition Tasks

BERT and its variants are the most performing models for named entity recognition (NER), a fundamental information extraction task. We must apply inference speedup methods for BERT-based NER models to be deployed in the industrial setting. Early exiting allows the model to use only the shallow layers to process easy samples, thus reducing the average latency. In this work, we introduce FastNER, a novel framework for early exiting with a BERT biaffine NER model, which supports both flat NER tasks and nested NER tasks. First, we introduce a convolutional bypass module to provide suitable features for the current layer's biaffine prediction head. This way, an intermediate layer can focus more on delivering high-quality semantic representations for the next layer. Second, we introduce a series of early exiting mechanisms for BERT biaffine model, which is the first in the literature. We conduct extensive experiments on 6 benchmark NER datasets, 3 of which are nested NER tasks. The experiments show that: (a) Our proposed convolutional bypass method can significantly improve the overall performances of the multi-exit BERT biaffine NER model. (b) our proposed early exiting mechanisms can effectively speed up the inference of BERT biaffine model. Comprehensive ablation studies are conducted and demonstrate the validity of our design for our FastNER framework.
Sentence-Level Event Detection Without Triggers via Prompt Learning and Machine Reading Comprehension

Sentence-level event detection has traditionally been carried out in two key steps: trigger identification and trigger classification. The trigger words first are identified from sentences and then utilized to categorize event types. However, this classification hugely relies on a substantial amount of annotated trigger words along with the accuracy of the trigger identification process. This annotation of trigger words is labor-intensive and time-consuming in real-world environments. As a solution to this, we propose a model that does not require any triggers for event detection. This model reformulates event detection into a two-tower model that uses machine learning comprehension and prompt learning. Compared to the existing methods, which are either trigger-based or trigger-free, experimental studies on two benchmark event detection datasets (ACE2005 and MAVEN) reveal that our proposed method can achieve competitive performance.
Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Abstract Background Social media platforms, especially Twitter, have turned out to be a major source of data repositories. They have become a platform that citizens can use to voice their concerns about issues that affect them. Most importantly, during the COVID-19 era, the platform was greatly used by governments and health organizations to sensitize people about the safety guidelines that they must adhere to so as to remain safe during the pandemic. As expected, people also used Twitter and other social media platforms to voice their opinions about how governments are handling the COVID-19 pandemic outbreak. Governments and organizations could, therefore, use these social media as a feedback mechanism that can help them know the view of the citizens about their policies. This could help them in making informed decisions about their policies. Aim The aim of this paper is to explore the use of BiLSTM deep learning technique for sentiment analysis of COVID-19 tweets. Methodology The study retrieved 197,327 tweets from the Nigeria Twitter domain using #COVID or #COVID-19 hashtags as keywords. The dataset was retrieved within the 1st month of COVID-19 vaccination in Nigeria, i.e., March 15–June 15, 2021. BiLSTM deep learning technique was trained using 789,306 sentiment annotated tweets obtained from Kaggle Sentiment140 tweet datasets. The preprocessed case study tweets were then used to evaluate the proposed model. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. Results With an accuracy of 78.29%, 98,545 (49.93%) positive sentiments and 98,782 negative sentiments (50.06%) were recorded. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. However, the presence of outliers which are tweets not related to COVID but which used the hashtag was observed. Conclusion This study has revealed the strength of BiLSTM deep learning technique for sentiment analysis. The results obtained revealed an almost balanced sentiments toward the pandemic with 49.93% positive disposition to the pandemic as compared to 50.06% negative disposition. This showed affirmed the impact of COVID vaccine in dousing citizen’s tension when it was made available for public use. However, the presence of outliers in the classified tweets could be a pointer to the reason why aspect-based sentiment analysis could be preferred to sentence-based sentiment analysis.