Reviewerly/exHarmony huggingface dataset:
Advancing nursing participation in user-centred design

What is the role of nursing in the digital health transformation of the 21st century? The answer to this critical question may rely on how prepared nursing is to enter into design processes associated with this evolution.The purpose of this paper is to introduce foundational terminology and tools to support increased nursing participation in user-centred design. Situated within a six-step design process, this includes a new analytic framework combining the disciplinary expertise of computer science with the nursing methodology Interpretive Description.The analytic framework and recommended research process were developed over the course of two projects each employing a similar collaborative mixed-methods design. Primary methodological drivers were drawn from the software development life-cycle and Interpretive Description in these digital health intervention studies.Using aspects of software development practice, an analytic framework was conceived as part of an interdisciplinary research process allowing nurses to integrate their disciplinary expertise in user-centred digital design. The framework allows nurses to parse collected data into a robust set of functional and non-functional requirements for software developers while still engaging in a fulsome interpretive analysis.There is a need for nursing to occupy a more significant role in the advancement of technology innovation in healthcare. However, a lack of familiarity with design-thinking and associated practical experience impedes nursing voices in this area. Tools and processes are introduced to enhance an existing nursing methodology as a means to extend our disciplinary design capacity.
A rotationally invariant semi-autonomous particle swarm optimizer with directional diversity

The semi-autonomous particle swarm optimizer (SAPSO) [1] is a relatively recent algorithm for global continuous optimization based on gradient direction and diversity controlling approach, providing autonomy for the particles and the swarm for exploiting regions in the search space, and preserving exploration during the whole search process. In the first study, although SAPSO algorithm holds the property rotational invariance in which it normally brings a lack of directional diversity in PSO context, the algorithm has shown very good performance in comparison to other PSO-like algorithms. In this paper, an improved version of SAPSO, named rotationally invariant SAPSO (RI-SAPSO), is proposed, which still holds the same property, but now it incorporates a rotation matrix generated by an exponential map to maintain directional diversity. A mathematical proof to prove that the RI-SAPSO algorithm is rotationally invariant is given. RI-SAPSO was evaluated on test functions extracted from CEC 2017 benchmark problems with six other PSO-like algorithms, along with its previous version. The comparative study was strengthened with a non-parametric Friedman's hypothesis test for 1 × k comparisons and p-values were adjusted in the post-hoc procedure. Simulation results showed that the proposed RI-SAPSO, in most problems, was able to find much better solutions and statistical significances were also observed.
A novel method to compute the weights of neural networks

Neural networks are the main strength of modern artificial intelligence; they have demonstrated revolutionary performance in a wide range of applications. In practice, the weights of neural networks are generally obtained indirectly using iterative training methods. Such methods are inefficient and problematic in many respects. Besides, neural networks trained end-to-end by such methods are typical black box models that are hard to interpret. Thus, it would be significantly better if the weights of a neural network could be calculated directly. In this paper, we located the key for calculating the weights of a neural network directly: assigning proper targets to the hidden units. Furthermore, if such targets are assigned, the neural network becomes a white box model that is easy to interpret. Thus, we propose a framework for solving the weights of a neural network and provide a sample implementation of the framework. The implementation was tested in various classification and regression experiments. Compared with neural networks trained using traditional methods, the constructed ones using solved weights had similar or better performance on many tasks, while remaining interpretable. Given the early stage of the proposed approach, many improvements are expectable in future developments.
Exploring the relationship between perceived technology-assisted teacher support and technology-embedded scientific inquiry: the mediation effect of hardiness

This study examines the correlations between perceived technology-assisted teacher support; student hardiness (i.e. individual commitment, challenge, and control); and technology-embedded scientific inquiry (TESI) using a mediational model approach. Data were gathered from the series of questionnaires administered to 1566 Chinese students with an average age of 11.5. Structural equation modelling (SEM) analysis results confirmed our assumption that when students perceived technology-assisted teacher support, this promoted their TESI hardiness, indirectly increasing their perceived TESI. Perceived technology-assisted teacher support and TESI were found to be positively correlated in this structural model. However, by including TESI hardiness in the mediational mechanism, its direct relationship with both perceived technology-assisted teacher support and perceived TESI was insignificant, while the mediating effects remained significant. Therefore, TESI hardiness is of considerable importance for delineating the relationship between perceived technology-assisted teacher support and perceived TESI. It was revealed that both the interpersonal variable (perceived technology-assisted teacher support) and the individual variable (TESI hardiness) are vital in determining the effectiveness of TESI. These findings enhance the understanding of and approach to the promotion of students’ perception of scientific inquiry by emphasising the value of technology-assisted teacher support and hardiness.
Robust Algorithms for Digital Image Correlation in the Presence of Displacement Discontinuities

Digital Image Correlation (DIC) is a well-known non-contact experimental technique. Its most common implementation is the subset-based approach, which performs a local least-squares fitting of a simple displacement model — usually an affine transform — to identify the displacement components at the center of the small area under investigation (the subset). Because of the statistical approach, DIC is usually able to provide reliable results even when theoretical prerequisites are not fully satisfied or in the presence of noise. However, the least-squares algorithm is not robust when the data set contains multiple statistical distributions. Indeed, the algorithm does not discriminate between them and process all the input data; thus, the resulting solution is usually unsatisfactory. A typical example implying the described context is the presence of cracks or shear bands inside the subset: as different sections of the inspected area move in different directions, the algorithm is unable to select a solution and the correlation between the reference and the test image is usually poor. This work proposes using RANSAC, a well-known robust algorithm, to select the largest domain of the subset. Because a similar problem has to be faced when computing strain components by the polynomial-fitting method, a simple modification of the main algorithm is suggested to handle also this problem.
From Domain-Specific Language to Code: Smart Contracts and the Application of Design Patterns

Due to the conceptual discrepancy between legal contract terms and code, understanding and creating smart contracts without errors can be difficult. This article proposes smart contract design patterns and their automated application through (the use of) a domain-specific language and code generation.
Attentive Multi-task Deep Reinforcement Learning

Sharing knowledge between tasks is vital for efficient learning in a multi-task setting. However, most research so far has focused on the easier case where knowledge transfer is not harmful, i.e., where knowledge from one task cannot negatively impact the performance on another task. In contrast, we present an approach to multi-task deep reinforcement learning based on attention that does not require any a-priori assumptions about the relationships between tasks. Our attention network automatically groups task knowledge into sub-networks on a state level granularity. It thereby achieves positive knowledge transfer if possible, and avoids negative transfer in cases where tasks interfere. We test our algorithm against two state-of-the-art multi-task/transfer learning approaches and show comparable or superior performance while requiring fewer network parameters.
Hierarchical structure correlation inference for pose estimation

Human pose estimation is a challenging and important basic subject in computer vision. At present, mainstream methods have achieved remarkable progress through the powerful representation capabilities of convolution neural networks. However, these methods don’t pay enough attention to the correlation of the structure of each part of the human body in pose estimation. To solve this problem, the present paper proposes the hierarchical structure correlation inference algorithm, composed of correlation inference modules, which increases the ability of the network to infer the correlation information of each part of the body. Moreover, this algorithm promotes the understanding of the human body structure. The proposed algorithm is effectively verified on the MPII Human Pose Dataset and Leeds Sport Pose Dataset, and the experiment shows that our results are better than current mainstream algorithms.
One-Class Classification in Images and Videos Using a Convolutional Autoencoder With Compact Embedding

In One-Class Classification (OCC) problems, the classifier is trained with samples of a class considered normal, such that exceptional patterns can be identified as anomalies.Indeed, for real-world problems, the representation of the normal class in the feature space is an important issue, considering that one or more clusters can describe different aspects of the normality.For classification purposes, it is important that these clusters be as compact (dense) as possible, for better discriminating anomalous patterns, which is a recurrent problem in OCC tasks.This work introduces a hybrid approach using deep learning and One-Class Support Vector Machine (OC-SVM) methods, named Convolutional Autoencoder with Compact Embedding (CAE-CE), for enhancing the compactness of clusters in the feature space.Such an approach is still underexplored in the literature, being restricted to models within the context of metric learning.Additionally, the absence of anomalous samples during training makes it difficult to determine when to interrupt the learning process, so as to avoid over-compression of the normal examples, thus resulting in overfitting of the model.In this work, we propose a novel sensitivity-based stop criterion, and its suitability for OCC problems was assessed.Using an OC-SVM for the classification task, several experiments were done using publicly available image and video datasets.We also introduce other two new benchmarks, specifically designed for video anomaly detection in highways.The final performance of the proposed method was compared with a baseline Convolutional Autoencoder (CAE).Overall results suggest that the enhanced compactness introduced by the CAE-CE improved the classification performance for most datasets.Also, the qualitative analysis of frames at the visual level indicated that features learned by CAE-CE are closely correlated to the anomalous events.
Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability

Dropout and DropConnect are useful methods to prevent multilayer neural networks from overfitting. In addition, it turns out that these tools can also be used to estimate the stability of networks regarding disturbances. Prototype based networks gain more and more attraction in current research because of their inherent interpretability and robust behavior. Popular prototype-based classifiers are support vector machines and the heuristically motivated Learning Vector Quantizer (LVQ). The Generalized Matrix LVQ (GMLVQ) is an extension of LVQ which can be interpreted as a special multilayer network containing a projection and a prototype layer. First in this paper, we extend the linear projection layer of GMLVQ to a non-linear mapping by employing different non-linear activations functions. Second, we compare the classification decision stabilities of the linear and the non-linear GMLVQ regarding DropConnect while taking the neural network perspective. Thus we can adopt DropConnect ideas known from multilayer perceptron learning to investigate stability and robustness of GMLVQ. To this end, the evaluation of the stability is done in terms of a information theoretic stability measure based on the Shannon-Entropy. We demonstrate the approach for three real world data sets from Raman spectroscopy, multi-spectral remote sensing and the well-known MNIST data set.
A deep neural network of multi-form alliances for personalized recommendations

The collaborative filtering adopted by traditional recommendation system has data sparsity problem, and the matrix decomposition method simply decomposes users and items into linear models for potential factors. These limitations have led to limited effectiveness for traditional recommendation algorithms. In this case, the recommendation system based on deep learning has emerged. Most of the current deep learning recommendations use deep neural networks to model some basic information, and in the modeling process, according to the input data categories, multiple mapping paths are used to map the original data to the potential vector space. However, these recommendations ignore that the alliance between different categories may have a potential impact on the recommendation effect. Aiming at this problem, this paper proposes a feedforward deep neural network of multi-form category features combination for recommendation, which is deep alliance neural network. According to the different alliance modes, it can be divided into deep series network (DSN), deep parallel network (DPN) and deep random network (DRN), which are used to solve the recommendation problem of implicit feedback. At the same time, a fusion model SMLP based on deep alliance neural network and traditional Multi-Layer Perceptron (MLP) is proposed to try to explore the performance of the fusion model. Finally, experiments on public datasets show that our proposed method significantly improves the existing methods. Empirical evidence indicates that deep series network and deep parallel network can provide better recommendation performance, while the recommended performance of deep random network and fusion model SMLP is not ideal. This indicates that the deep alliance network needs to pay special attention to the order of the category features in the process of category features association.
A Text Document Clustering Method Based on Weighted BERT Model

Traditional text document clustering methods represent documents with uncontextualized word embeddings and vector space model, which neglect the polysemy and the semantic relation between words. This paper presents a novel text document clustering method to deal with these problems. Firstly, pre-trained language representation model Bidirectional Encoder Representations from Transformers (BERT) is utilized to generate sentence embeddings. Then, two sentence-level weighting schemes based on named entity are designed to enhance the performance. Finally, the k-means clustering algorithm is applied to find groups of similar documents. Experimental results on four datasets indicate that the proposed weighted method achieves higher accuracy than unweighted average method. Friedman tests conducted separately with F1 score and Adjusted Rand Index (ARI) values both validate better overall performance of our proposed method.
Blockchain-Based Decentralized and Secure Lightweight E-Health System for Electronic Health Records

Electronic health record (EHR) is a digital format of patient health information which typically contains patient contact information, vital signs, medical history, current and past medication that are outsourced to cloud. But the data leakage in cloud-based EHR causes serious privacy and security issues on various health care organizations. Existing cryptographic methods are not sufficient to address data breaches of EHR data in cloud. Blockchain technology is an emerging technology to solve privacy and security issues of EHR data in a decentralized manner on cloud. In this paper, we have designed a secure decentralized cloud-based medical block chain (CMBC) to solve privacy and security issues when exchanging the patient health care data to different medical organizations. Before uploading the EHR data to cloud-based block chain, the data are encrypted using lightweight authentication encryption algorithm AES_256_GCM to achieve better performance in health care. Based on the encrypted data on blockchain, Etherum based solidity smart code to restrict the access of EHR data on cloud. The medical blockchain also maintains the interoperability, traceability and anonymity of patients EHR data between various organizations. So the secure decentralized cloud-based medical block chain visualizes the patient EHR data in decentralized, distributed and immutable environment with improved security.
TextCSN

Clustering is a key problem in several applications. Although this task is originally unsupervised, there are many proposals leveraging different supervision signals in order to improve clustering performance. Some semi-supervised clustering methods employ pairwise constraints to inform the learning algorithm about pairs of instances that should be in the same cluster (must-link constraints or similar instances) and pairs that should be in different clusters (cannot-link constraints or dissimilar instances). In many applications, to provide pairwise constraints is cheaper than asking users for explicit labels on the data. More recently, deep clustering methods have been proposed in the literature. Such methods consists in learning a deep neural representation of the input data in order to improve clustering. In this paper, we present TextCSN, a deep clustering approach that combines (i) a Convolutional Siamese Network (CSN) based on pairwise constraints to perform representation learning and (ii) the traditional K-Means algorithm for unsupervised clustering using the learned representation. As far as we know, this is the first semi-supervised deep learning method based on pairwise constraints applied on text clustering. By means of eight text clustering tasks, we assess our approach comparing it with two baselines: MPC-KMeans, a semi-supervised clustering algorithm; and ordinary K-Means algorithm. Results indicate that the proposed approach outperforms the baselines in six of these datasets, and its performance increases with the number of constraints provided.
Personality-Guided Cloud Pricing via Reinforcement Learning

As an efficient commercial computing paradigm, cloud computing provides various computing and storage resources to users in a pay-as-you-go manner. However, existing cloud pricing models and mechanisms are deterministic to some degree, thus, may not work well in a real-world environment where user perceived values with respect to cloud services are dynamically changing and highly personalized. In this article, we develop a reinforcement learning (RL)-based dynamic cloud pricing scheme to optimize both cloud provider’s profit and costs of heterogeneous users with distinct personalities. Specifically, we first propose a novel personality-guided user perceived value prediction scheme to proactively capture the dynamics of the users’ perceived values with respect to cloud services. The prediction scheme models the relationship among user personality, service price, quality of service (QoS), user satisfaction and perceived value in the cloud service market. Second, on the basis of the prediction model, a RL-based cloud pricing mechanism is developed to learn sequential service pricing decision-making for profit and costs optimization. Particularly, the profit and costs optimization problem is modeled as a discrete-time Markov decision process (MDP) that is solved by using Q-learning. Finally, extensive simulation experiments have been conducted to verify our user perceived value prediction scheme and RL-based cloud service pricing mechanism. Simulation results show that our perceived value prediction scheme can achieve up to 87.50 percent prediction accuracy, and our RL-based pricing mechanism can obtain up to 19.39 percent more profit than the state-of-the-art scheme.
Ellipse fitting by spatial averaging of random ensembles

Earlier ellipse fitting methods often consider the algebraic and geometric forms of the ellipse. The work presented here makes use of an ensemble to provide better results. The method proposes a new ellipse parametrization based on the coordinates of both foci, and the distance between them and each point of the ellipse where the Euclidean norm is applied. Besides, a certain number of subsets are uniformly drawn without replacement from the overall training set which allows estimating the center of the distribution robustly by employing the L1 median of each estimated focus. An additional postprocessing stage is proposed to filter out the effect of bad fits. In order to evaluate the performance of this method, four different error measures were considered. Results show that our proposal outperforms all its competitors, especially when higher levels of outliers are presented. Several synthetic and real data tests were developed and confirmed such finding.
A Modulation Recognition Algorithm based on Cyclic Spectrum and SVM Classification

The modulation recognition algorithm identifies the modulation mode of the communication signal to determine the signal type contained in the communication signal. A modulation recognition algorithm based on cyclic spectrum and support vector machine classification is designed. The algorithm includes two aspects: feature extraction and classifier design. By selecting the second-order cyclic spectrum feature of signal with high separability and strong stability, and the statistical moment feature of signal amplitude realized in frequency domain, the algorithm is realized by using support vector machine classifier according to the selected feature. Finally, the experiment simulation is carried out.