Reviewerly/exHarmony huggingface dataset:
Digital Competencies in Selected European Countries among University and High-School Students: Programming is lagging behind

Abstract Background: Constant integration of digital technologies in economic and social life is rapidly and significantly shaping and changing our environment and ourselves. To function in such a world, even in daily routines, it is necessary to possess certain digital competencies. Objectives: This paper aims to examine how university and high-school students of economic orientations from selected European countries self-assess their digital competencies, and to analyse the identified differences. This will enable further understanding of university and high-school students’ digital competencies that can serve as guidance for improving teaching practices and curricula. Methods/Approach: A survey was conducted to collect data that were analysed using non-parametric statistic tests (Mann-Whitney U test and Kruskal-Wallis H test) and Spearman Rank-Order Correlation coefficient. Results: University and high-school students consider to have below intermediate level of digital competencies. High-school students self-assessed digital competencies at a higher level than university students. University students of higher years of study self-assessed digital competencies at a higher level. There is no universal pattern among high-school students of different years of study. University students in the Accounting module and high-school students in the Tourism module assessed their digital competencies at the lowest level in several areas. There is a consistency in self-assessment of digital knowledge and digital skills. Conclusions: The identified below intermediate level of digital competencies and discovered discrepancies indicated the need for educational process improvements to provide university and high-school students with a higher degree of digital competencies. Programming is the most lagging behind in all the observed groups.
Graph coloring with physics-inspired graph neural networks

We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multiclass node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts model. Generalizations to other multiclass problems such as community detection, data clustering, and the minimum clique cover problem are straightforward. We provide numerical benchmark results and illustrate our approach with an end-to-end application for a real-world scheduling use case within a comprehensive encode-process-decode framework. Our optimization approach performs on par or outperforms existing solvers, with the ability to scale to problems with millions of variables.
A Sign Language Recognition System Applied to Deaf-Mute Medical Consultation

It is an objective reality that deaf-mute people have difficulty seeking medical treatment. Due to the lack of sign language interpreters, most hospitals in China currently do not have the ability to interpret sign language. Normal medical treatment is a luxury for deaf people. In this paper, we propose a sign language recognition system: Heart-Speaker. Heart-Speaker is applied to a deaf-mute consultation scenario. The system provides a low-cost solution for the difficult problem of treating deaf-mute patients. The doctor only needs to point the Heart-Speaker at the deaf patient and the system automatically captures the sign language movements and translates the sign language semantics. When a doctor issues a diagnosis or asks a patient a question, the system displays the corresponding sign language video and subtitles to meet the needs of two-way communication between doctors and patients. The system uses the MobileNet-YOLOv3 model to recognize sign language. It meets the needs of running on embedded terminals and provides favorable recognition accuracy. We performed experiments to verify the accuracy of the measurements. The experimental results show that the accuracy rate of Heart-Speaker in recognizing sign language can reach 90.77%.
Facial expression recognition based on improved depthwise separable convolutional network

A single network model can't extract more complex and rich effective features. Meanwhile, the network structure is usually huge, and there are many parameters and consume more space resources, etc. Therefore, the combination of multiple network models to extract complementary features has attracted extensive attention. In order to solve the problems existing in the prior art that the network model can't extract high spatial depth features, redundant network structure parameters, and weak generalization ability, this paper adopts two models of Xception module and inverted residual structure to build the neural network. Based on this, a face expression recognition method based on improved depthwise separable convolutional network is proposed in the paper. Firstly, Gaussian filtering is performed by Canny operator to remove noise, and combined with two original pixel feature maps to form a three-channel image. Secondly, the inverted residual structure of MobileNetV2 model is introduced into the network structure. Finally, the extracted features are classified by Softmax classifier, and the entire network model uses ReLU6 as the nonlinear activation function. The experimental results show that the recognition rate is 70.76% in Fer2013 dataset (facial expression recognition 2013) and 97.92% in CK+ dataset (extended Cohn Kanade). It can be seen that this method not only effectively mines the deeper and more abstract features of the image, but also prevents network over-fitting and improves the generalization ability.
Comparison of multi-criteria decision-making methods for online controlled experiments in a launch decision-making framework

User-intensive software systems such as Web and mobile applications are defined as systems that serve and interact with an increasingly large number of users. Inefficient launch decisions in user-intensive systems in domains such as social media, information retrieval and e-commerce can lead to dramatic loss in the goal metrics of these highly scalable applications, and therefore impact potentially billions of users. Due to the complexity of user-intensive systems, engineers rely heavily on A/B testing (i.e., online controlled experiments) to evaluate and measure the impact of new changes. However, little attention has been paid to improve the empirical process of making launch decisions based on the A/B testing results. In this paper, we propose a framework to address this issue. We propose a Multi-Criteria Decision Making (MCDM) framework that uses A/B testing results to provide launch decisions analysis, as a complementary tool to assist decision making. The framework includes modules for 1) configuration setup, 2) criteria weighting, 3) pairwise comparison between criteria and alternatives 4) analysis of alternatives using MCDM and produces launch decisions based on the A/B testing results. Experimental results from publicly available dataset that compares well-known and widely applied MDCM methods shows that a good combination of the Analysis of Alternative method (such as TOPSIS-Vector, MMOORA, and VIKOR) and Criteria Weighting method (such as Standard Deviation) in the framework can effectively assistlaunch decision making. We formulate the problem of launch decision making using A/B testing results and propose a MCDM based framework for it, as an imperfect first step to address this problem. The experiments suggest that MCDM methods such as TOPSIS, MMOORA and VIKOR may be effective at making launch decisions based on A/B testing results.
EHDFL: Evolutionary hybrid domain feature learning based on windowed fast Fourier convolution pyramid for medical image classification

With the development of modern medical technology, medical image classification has played an important role in medical diagnosis and clinical practice. Medical image classification algorithms based on deep learning emerge in endlessly, and have achieved amazing results. However, most of these methods ignore the feature representation based on frequency domain, and only focus on spatial features. To solve this problem, we propose a hybrid domain feature learning (HDFL) module based on windowed fast Fourier convolution pyramid, which combines the global features with a wide range of receptive fields in frequency domain and the local features with multiple scales in spatial domain. In order to prevent frequency leakage, we construct a Windowed Fast Fourier Convolution (WFFC) structure based on Fast Fourier Convolution (FFC). In order to learn hybrid domain features, we combine ResNet, FPN, and attention mechanism to construct a hybrid domain feature learning module. In addition, a super-parametric optimization algorithm is constructed based on genetic algorithm for our classification model, so as to realize the automation of our super-parametric optimization. We evaluated the newly published medical image classification dataset MedMNIST, and the experimental results show that our method can effectively learning the hybrid domain feature information of frequency domain and spatial domain.
Hand gesture teleoperation for dexterous manipulators in space station by using monocular hand motion capture

Space robots in the space station can assist or substitute astronauts to conduct some in-cabin tasks to alleviate the problems of heavy tasks and the limited number of astronauts in the space station. In response to these problems, a hand gesture teleoperation method is proposed for controlling of dexterous manipulators in the space station, which is based on monocular hand motion capture and video-calling tools. This method can realize free and convenient teleoperation. The work is mainly divided into two parts, which are monocular hand motion capture and teleoperation framework for dexterous manipulators. For monocular hand motion capture, a hand pose estimation method based on hand biological constraints from single RGB images is designed. On the other hand, a dexterous manipulator teleoperation framework based on hand motion capture and video-calling tools is designed. Experimental results carried out on hand pose estimation datasets proved that the proposed hand pose estimation method outperforms the state-of-the-art methods. Physical experiment on a space dexterous manipulator teleoperation platform validated the effectiveness of the proposed hand gesture teleoperation method.
A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases

This systematic literature aims to identify soft computing techniques currently utilized in diagnosing tropical febrile diseases and explore the data characteristics and features used for diagnoses, algorithm accuracy, and the limitations of current studies. The goal of this study is therefore centralized around determining the extent to which soft computing techniques have positively impacted the quality of physician care and their effectiveness in tropical disease diagnosis. The study has used PRISMA guidelines to identify paper selection and inclusion/exclusion criteria. It was determined that the highest frequency of articles utilized ensemble techniques for classification, prediction, analysis, diagnosis, etc., over single machine learning techniques, followed by neural networks. The results identified dengue fever as the most studied disease, followed by malaria and tuberculosis. It was also revealed that accuracy was the most common metric utilized to evaluate the predictive capability of a classification mode. The information presented within these studies benefits frontline healthcare workers who could depend on soft computing techniques for accurate diagnoses of tropical diseases. Although our research shows an increasing interest in using machine learning techniques for diagnosing tropical diseases, there still needs to be more studies. Hence, recommendations and directions for future research are proposed.
Blockchain-Based Trust and Reputation Management in SIoT

In the Social Internet of Things (SIoT), trust refers to the decision-making process used by the trustor (Service Requesters (SRs) or Service Consumers (SCs)) to decide whether or not to entrust the trustee (Service Providers (SPs)) with specific services. Trust is the key factor in SIoT domain. The designing of a two-way, two-stage parameterized feedback-based, service-driven, attacks-resistant trust and reputation system for SIoT accompanied by a penalty mechanism for dishonest SPs and SRs is our main contribution that mitigates the trust-related issues occurring during service provisioning and service acquisition amongst various entities (SPs or SRs) and enhances trust amongst them. Our proposed methodology examines a SP’s local trust, global trust, and reputation by taking into account “Social Trust” and “Quality of Service (QoS)” factors”. Two—Stage Parameterized feedback” is incorporated in our proposed strategy to better manage “intention” and “ability” of SRs and provides early identification of suspicious SRs. This feature compels SRs to act honestly and rate the corresponding SPs in a more accurate way. Our recommended paradigm sorts SPs into three SP status lists (White List, Grey List, and Black List) based on reputation values where each list has a threshold with respect to the maximum service fee that can be charged. SPs in White List charge the most per service. SPs in other lists have a lower selection probability. Every feedback updates the SP’s trust and reputation value. Sorting SPs increases resistance against On Off Attack, Discriminatory Attack, Opportunistic Service Attack, and Selective Behavior Attacks. SPs must operate honestly and offer the complete scope of stated services since their reputation value relies on all their global trust values (Tglobal) for various services. Service requests may be accepted or denied by SPs. “Temporarily banned” SRs can only request unblocked services. SRs lose all privileges once on a “permanently banned” list. If local and global trust values differ by more than the threshold, the SR is banned. Our method also provides resistance against Bad Mouthing Attack, Ballot Stuffing Attack. Good Mouthing Attack/Self—Propagating Attack. Experiments indicate our trust and reputation management system recognizes and bans fraudulent SRs. “Dishonest SPs” are “blacklisted,” which affects their reputation, trust, and service charges.
Blockchain-Based Data Sharing With Key Update for Future Networks

Future networks incorporate artificial intelligence to enable smart resource management and adaptive service provisioning. With a heterogeneous architecture and a large number of users in future networks, transparent and decentralized data sharing is required to promote data circulation and break data silos, for which blockchain is a potential solution to allow intelligent access permission control. However, it remains a challenging task to achieve flexible authorization management for blockchain-based data sharing and efficient key update for multi-users in case of key exposure. In this paper, we propose an intelligent blockchain-based data-sharing scheme with key update for future networks. First, we design a new encryption scheme, where keywords of data are extracted using machine learning algorithms that are published on the blockchain. Then, keywords of data and time validity are used to encrypt different types of data for flexible data authorization. Second, using hierarchical identity-based encryption, we construct an efficient key update mechanism, where update tokens are generated by invoking a smart contract deployed on the blockchain to facilitate key and ciphertext updates. We formally prove that the proposed scheme can guarantee three essential security properties: forward security, post-compromise security, and collusion attack resistance. On-chain and off-chain experiment results are provided to demonstrate that the proposed scheme can achieve computational and communication efficiency for key and ciphertext updates.
Interpretable brain disease classification and relevance-guided deep learning

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training. The method was applied using T1-weighted MR images from 128 subjects with Alzheimer's disease (mean age = 71.9 ± 8.5 years) and 290 control subjects (mean age = 71.3 ± 6.4 years). The developed relevance-guided framework achieves higher classification accuracies than conventional CNNs but more importantly, it relies on less but more relevant and physiological plausible voxels within brain tissue. Additionally, preprocessing effects from skull stripping and registration are mitigated. With the interpretability of the decision mechanisms underlying CNNs, these results challenge the notion that unprocessed T1-weighted brain MR images in standard CNNs yield higher classification accuracy in Alzheimer's disease than solely atrophy.
problexity—An open-source Python library for supervised learning problem complexity assessment

The problem’s complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning – becoming the basis for determining meta-attributes or multi-criteria optimization – allowing the evaluation of the training set resampling without needing to rebuild the recognition model. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 classification complexity measures and 12 regression complexity measures for the Python language – compatible with the scikit-learn programming interface – allowing for the implementation of research using them in the most popular programming environment of the machine learning community.
STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.
CJT-DEO: Condorcet’s Jury Theorem and Differential Evolution Optimization based ensemble of deep neural networks for pulmonary and Colorectal cancer classification

Cancer is one of the most dangerous diseases globally, causing adverse effects on human life, with early detection and treatment planning being crucial for patients. Amongst different malignancies, Lung and Colorectal cancer cause the first and second most cancer deaths in the world, respectively. In this study, the authors aim to analyze LC25000 histopathological image dataset for lung and colon cancer detection. The fundamental goal of the proposed research is to leverage the ensemble learning approach to improve the classification performance of deep learning models. Many previous studies have proposed several ensemble methods and weighting schemes. However, none of them optimized the assigned weights using a meta-heuristic-based approach as per our best knowledge. The authors have applied Differential Evolution optimization to optimize and find the optimal assigned weights to the classifiers while training the ensemble model. In addition, a novel approach to ensemble base learners with majority voting based on Condorcet's Jury Theorem has also been proposed. This proposed method has been shown to save a lot of computational efforts by eliminating the training procedure of meta-learners. Besides this, the authors also demonstrated that Condorcet's Jury Theorem holds while ensembling the N number of classifiers in Neural Networks. Our proposed method and experimental results outperformed compared to the state-of-the-art with the optimized ensemble model showing an accuracy of 99.78% and Condorcet's Jury Theorem-based ensemble model 99.88% on 5-class classification.
Shape-constrained multi-objective genetic programming for symbolic regression

We describe and analyze algorithms for shape-constrained symbolic regression, which allow the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering — in particular, when data-driven models, which are based on data of measurements must exhibit certain properties (e.g. positivity, monotonicity, or convexity/concavity). To satisfy these properties, we have extended multi-objective algorithms with shape constraints. A soft-penalty approach is used to minimize both the constraint violations and the prediction error. We use the non-dominated sorting genetic algorithm (NSGA-II) as well as the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The algorithms are tested on a set of models from physics textbooks and compared against previous results achieved with single objective algorithms. Further, we generated out-of-domain samples to test the extrapolation behavior using shape constraints and added a different level of noise on the training data to verify if shape constraints can still help maintain the prediction errors to a minimum and generate valid models. The results showed that the multi-objective algorithms were capable of finding mostly valid models, also when using a soft-penalty approach. Further, we investigated that NSGA-II achieved the best overall ranks on high noise instances.