Reviewerly/exHarmony huggingface dataset:
Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study

Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688-0.768) with the sensitivity of 66.2% (95% CI 58.2-73.6) and specificity of 66.8% (95% CI 64.6-68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545-0.737), and sensitivity and specificity were 34.2% (95% CI 19.6-51.4) and 88.8% (95% CI 85.6-91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681-0.844) with the sensitivity of 63.2% (95% CI 46-78.2) and specificity of 80.5% (95% CI 76.6-84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
Fitting missing API puzzles with machine translation techniques

While implementing software projects, developers do not reinvent the wheel but try to reuse existing API calls and source code. In recent years, the problems related to recommending APIs and code snippets have been intensively investigated. Although current approaches have achieved encouraging performance, there is still the need to improve the recommendation process’s effectiveness and efficiency. In this work, we reformulate the problem of API recommendations by proposing learning and recommending API sequences relevant to a given coding context. We present LUPE, a novel approach to API and code recommendation, exploiting cutting-edge deep learning techniques. Thanks to the underlying Encoder–Decoder architecture specialized in transforming sequences, LUPE can effectively learn the order in which invocations occur. The approach has been evaluated on two Android datasets and compared with GAPI and FACER, two state-of-the-art API recommender systems. Being fed with augmented training data, our conceived approach can obtain a high prediction accuracy, and produce a perfect match in several cases, hence outperforming the baselines.
SBSS: Stacking-Based Semantic Segmentation Framework for Very High-Resolution Remote Sensing Image

Semantic segmentation of Very High Resolution (VHR) remote sensing images is a fundamental task for many applications. However, large variations in the scales of objects in those VHR images pose a challenge for performing accurate semantic segmentation. Existing semantic segmentation networks are able to analyse an input image at up to four resizing scales, but this may be insufficient given the diversity of object scales. Therefore, Multi Scale (MS) test-time data augmentation is often used in practice to obtain more accurate segmentation results, which makes equal use of the segmentation results obtained at the different resizing scales. However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation. Based on this behaviour, a Stacking-Based Semantic Segmentation (SBSS) framework is proposed to improve the segmentation results by learning this behaviour, which contains a learnable Error Correction Module (ECM) for segmentation result fusion and an Error Correction Scheme (ECS) for computational complexity control. Two ECS, i.e., ECS-MS and ECS-SS, are proposed and investigated in this study. The Floating-point operations (Flops) required for ECS-MS and ECS-SS are similar to the commonly used MS test and the Single-Scale (SS) test, respectively. Extensive experiments on four datasets (i.e., Cityscapes, UAVid, LoveDA and Potsdam) show that SBSS is an effective and flexible framework. It achieved higher accuracy than MS when using ECS-MS, and similar accuracy as SS with a quarter of the memory footprint when using ECS-SS.
Assembly of countersunk-hole parts based on 3D pose estimation and impedance control

Abstract：For robotic assembly long cylindrical shaft and countersunk-hole parts，this paper investigates the assembly strategies of combining 3D vision，and force perception for compliantly assembling parts in arbitrary positions and poses during assembly.An algorithm is proposed based 3D vision for pose estima• tion of countersunk-hole parts：firstly，use supervoxel segmentation，Constrained Planar Cuts(CPC)and clustering to complete the preprocessing of point cloud，and then use Weight-based Random Sample Con• sensus(WRANSAC)plane fitting to acquire plane pose，finally，estimate the assembly position by profile extraction and circle fitting of countersunk-hole.In addition，we implement the compliant assembly of the parts using impedance control.The experimental results indicate that the average distance error ∆ d of WRANSAC plane fitting algorithm can be as high as 0. 09 mm and the angular error ∆ θ can be as high as 1. 8°.Moreover，the angular error of the countersunk-hole position in 3D space is 1. 0°，which can realize the assembly of parts with a mating clearance of 0. 5 mm.The assembly strategy meets the requirements 文章编号 1004-924X (2022
Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm

Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required. In this article, we experiment with Nonlinear Autoregressive Neural Networks (NARNN), which are a type of RNN, and we use the Discrete Mycorrhizal Optimization Algorithm (DMOA) in the optimization of the NARNN architecture. We used the Mackey-Glass chaotic time series (MG) to test the proposed approach, and very good results were obtained. In addition, some comparisons were made with other methods that used the MG and other types of Neural Networks such as Backpropagation and ANFIS, also obtaining good results. The proposed algorithm can be applied to robots, microsystems, sensors, devices, MEMS, microfluidics, piezoelectricity, motors, biosensors, 3D printing, etc.
Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing

Abstract Secure multiparty computation (MPC) is a cryptographic tool that allows computation on top of sensitive biomedical data without revealing private information to the involved entities. Here, we introduce Sequre, an easy-to-use, high-performance framework for developing performant MPC applications. Sequre offers a set of automatic compile-time optimizations that significantly improve the performance of MPC applications and incorporates the syntax of Python programming language to facilitate rapid application development. We demonstrate its usability and performance on various bioinformatics tasks showing up to 3–4 times increased speed over the existing pipelines with 7-fold reductions in codebase sizes.
Federated Meta-Learning Framework for Few-shot Fault Diagnosis in Industrial IoT

Learning-based mechanical fault diagnosis (FD) methods have been widely investigated in recent years. To overcome the shortages of centralized learning techniques from the perspective of data privacy and high communication overhead, federated learning (FL) is emerging as a promising method for FD. However, a large number of labeled fault data is required for the FL technique, which is not accessible in real-world industrial Internet-of-Things (IIoT) scenarios. To address the data scarcity challenge (i.e., few-shot), we propose a collaborative learning method that incorporates meta-learning into the federated learning framework. Specifically, our approach learns an effectively global meta-learner, which can quickly adapt to a new machine or a newly encountered fault category with just a few labeled examples and training iterations. Further, we theoretically analyze the convergence of the proposed algorithm in a non-convex setting. We conduct an extensive empirical evaluation of two real-world fault diagnosis datasets and they demonstrate that our proposed method achieves significantly faster convergence and higher accuracy, compared with the existing approaches.
Triple-BigGAN: Semi-supervised generative adversarial networks for image synthesis and classification on sexual facial expression recognition

Automatic recognition of facial images showing erotic expressions can help to understand our social interaction and to detect non-appropriate images even when there is no nakedness present in them. This paper contemplates, for the first time, to exploit facial cues applied to automatic Sexual Facial Expression Recognition (SFER). With this goal, we introduce a new dataset named Sexual Expression and Activity Faces (SEA-Faces-30k) for SFER, which contains 30k manually labeled images under three categories: erotic, suggestive-erotic, and non-erotic. Deep Convolutional Neural Networks require large-scale annotated image datasets with diversity and variations to be properly trained. Unfortunately, gathering such a massive amount of data is not feasible in this area. Therefore, we present a new semi-supervised GAN framework named Triple-BigGAN, which learns a generative model and a classifier simultaneously. It learns both tasks in an end-to-end fashion while using unlabeled or partially labeled data. The Triple-BigGAN framework shows promising classification performance for the SFER task (i.e., 93.59%) and other five benchmark datasets, i.e., FER-2013, CIFAR-10, Expression in-the-Wild (ExpW), Modified National Institute of Standards and Technology database (MNIST), and Street View House Numbers (SVHN). Next, we evaluated the quality of samples generated by Triple-BigGAN with a resolution of 256×256 pixels using Inception Score (IS) and Frechet Inception Distance (FID). Our approach obtained the best FID (i.e., 19.94%) and IS (i.e., 97.98%) scores on the SEA-Faces-30k dataset. Further, we empirically demonstrated that synthetic erotic face images generated by Triple-BigGAN could also help in improving the classification performance of deep supervised networks.
FrodoPIR: Simple, Scalable, Single-Server Private Information Retrieval

We design FrodoPIR — a highly configurable, stateful, single-server Private Information Retrieval (PIR) scheme that involves an offline phase that is completely client-independent. Coupled with small online overheads, it leads to much smaller amortized financial costs on the server-side than previous approaches. In terms of performance for a database of 1 million 1KB elements, FrodoPIR requires &lt; 1 second for responding to a client query, has a server response size blow-up factor of &lt; 3.6x, and financial costs are ~$1 for answering 100,000 client queries. Our experimental analysis is built upon a simple, non-optimized Rust implementation, illustrating that FrodoPIR is particularly suitable for deployments that involve large numbers of clients.
A Model to Measure U-Learning in Virtual Higher Education: U-CLX

Ubiquitous learning is an evolution of educational learning processes that implements the concept of ubiquity. That is to say, it is found at all times and in all places. This article summarizes our previous works and proposes an alternative to answer our main research question: how can we develop a U-Learning model that integrates connective learning and xAPI user experiences? This paper presents the U-Learning Model Supported by Learning Experiences and Connective Learning for virtual higher education (U-CLX Model) to measure U-Learning in virtual institutions. The U-CLX Model measures ubiquitous learning in four dimensions: time, place, medium, and context. To develop the model, we proposed a theoretical and technological framework, a definition of the U-Learning concept, a unit of measurement for ubiquitous learning (UbiquoL), and a description of the measurement process. We validated the proposal by thematic specialists and applied the instrument in two universities. The model aims to assess the level of ubiquitous learning in virtual higher education institutions and to suggest how these institutions can improve within their current operations.
Similarity Feature Construction for Matching Ontologies through Adaptively Aggregating Artificial Neural Networks

Ontology is the kernel technique of Semantic Web (SW), which enables the interaction and cooperation among different intelligent applications. However, with the rapid development of ontologies, their heterogeneity issue becomes more and more serious, which hampers communications among those intelligent systems built upon them. Finding the heterogeneous entities between two ontologies, i.e., ontology matching, is an effective method of solving ontology heterogeneity problems. When matching two ontologies, it is critical to construct the entity pair’s similarity feature by comprehensively taking into consideration various similarity features, so that the identical entities can be distinguished. Due to the ability of learning complex calculating model, recently, Artificial Neural Network (ANN) is a popular method of constructing similarity features for matching ontologies. The existing ANNs construct the similarity feature in a single perspective, which could not ensure its effectiveness under diverse heterogeneous contexts. To construct an accurate similarity feature for each entity pair, in this work, we propose an adaptive aggregating method of combining different ANNs. In particular, we first propose a context-based ANN and syntax-based ANN to respectively construct two similarity feature matrices, which are then adaptively integrated to obtain a final similarity feature matrix through the Ordered Weighted Averaging (OWA) and Analytic hierarchy process (AHP). Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track are used to verify the effectiveness of our method. The experimental results show that our approach’s results are better than single ANN-based ontology matching techniques and state-of-the-art ontology matching techniques.
Robust Two-Qubit Gates for Trapped Ions Using Spin-Dependent Squeezing

Entangling gates are an essential component of quantum computers. However, generating high-fidelity gates, in a scalable manner, remains a major challenge in all quantum information processing platforms. Accordingly, improving the fidelity and robustness of these gates has been a research focus in recent years. In trapped ions quantum computers, entangling gates are performed by driving the normal modes of motion of the ion chain, generating a spin-dependent force. Even though there has been significant progress in increasing the robustness and modularity of these gates, they are still sensitive to noise in the intensity of the driving field. Here we supplement the conventional spin-dependent displacement with spin-dependent squeezing, which creates a new interaction, that enables a gate that is robust to deviations in the amplitude of the driving field. We solve the general Hamiltonian and engineer its spectrum analytically. We also endow our gate with other, more conventional, robustness properties, making it resilient to many practical sources of noise and inaccuracies.
The Role of Innovation Capability in the relationship between Entrepreneurial Leadership and Innovation Performance in the SMEs Service industry

Drawing on the resource-based view (RBV) and the dynamic capabilities view (DCV), this study aimed to investigate the mediating role of innovation capability (IC) on the relationship between entrepreneurial leadership (EL) and innovation performance (IP). Survey data were collected from 321 middle-level employees in the SME service industry. Structural equation modeling (SEM) was used to assess the proposed relationships. The empirical analysis revealed that entrepreneurial leadership impacted innovation performance directly and indirectly through the mediating role of innovation capability. The current research contributes to both RBV and DCV by proposing and empirically testing how entrepreneurial leaders affect innovation performance. The results from this research suggest that service SME managers can play a crucial role in promoting their firms' innovation capabilities, which, in turn, influences the firms' innovation performance. This study emphasizes EL's innovation effectiveness and the triggering process of innovation capability and provides various solutions for firms to demonstrate leadership and innovation practices in responding to uncertain environments.
Comparison of Deep Learning Models for Automatic Detection of Sarcasm Context on the MUStARD Dataset

Sentiment analysis is a major area of natural language processing (NLP) research, and its sub-area of sarcasm detection has received growing interest in the past decade. Many approaches have been proposed, from basic machine learning to multi-modal deep learning solutions, and progress has been made. Context has proven to be instrumental for sarcasm and many techniques that use context to identify sarcasm have emerged. However, no NLP research has focused on sarcasm-context detection as the main topic. Therefore, this paper proposes an approach for the automatic detection of sarcasm context, aiming to develop models that can correctly identify the contexts in which sarcasm may occur or is appropriate. Using an established dataset, MUStARD, multiple models are trained and benchmarked to find the best performer for sarcasm-context detection. This performer is proven to be an attention-based long short-term memory architecture that achieves an F1 score of 60.1. Furthermore, we tested the performance of this model on the SARC dataset and compared it with other results reported in the literature to better assess the effectiveness of this approach. Future directions of study are opened, with the prospect of developing a conversational agent that could identify and even respond to sarcasm.
An effective method for figures and tables detection in academic literature

Figures and tables in scientific articles serve as data sources for various academic data mining tasks. These tasks require input data to be in its entirety. However, existing studies measure the performance of algorithms using the same IoU (Intersection over Union) or IoU-based metrics that are used for natural situations. There is a gap between high IoU and detection entirety in scientific figures and tables detection tasks. In this paper, we demonstrate the existence of this gap and suggest that the leading cause is the detection error in the boundary area. We propose an effective detection method that cascades semantic segmentation and contour detection. The semantic segmentation model adopted a novel loss function to enhance the weights of boundary parts and a categorized dice metric to evaluate the imbalanced pixels in the segmentation result. Under rigorous testing criteria, the method proposed in this paper yielded a page-level F1 of 0.983 exceeding state-of-the-art academic figure and table detection methods. The research results in this paper can significantly improve the data quality and reduce data cleaning costs for downstream applications.
BE-TRDSS: Blockchain-Enabled Secure and Efficient Traceable-Revocable Data-Sharing Scheme in Industrial Internet of Things

As an important component of the Industrial Internet of Things (IIoT), the smart factory uses IIoT and equipment-monitoring technology to collect data to reasonably arrange the production. A large number of data is collected and uploaded to the IIoT cloud platform. However, the IIoT cloud platform is semitrusted and has structural limitations and vulnerability, which makes it necessary to realize data dynamic security sharing and malicious users' tracking. In this article, we show that the most recent work on this issue is still vulnerable to security threats at first. Then, a blockchain-enabled dynamic and traceable data-sharing scheme for a smart factory is proposed. Blockchain performs the user authentication and stores the ciphertext index and public keys to avoid tampering with shared data. The tracking algorithm tracks malicious users and adds them to a revocation list embedded in the ciphertext. And the authority can flexibly select domain or user revocation as required. The LSSS access policy is hidden to protect user privacy, and the cloud server uses the match test algorithm to detect whether users meet the hidden access policy. Additionally, online–offline encryption and outsourced decryption improve the efficiency of the scheme where the ciphertext and the pairing operations required for decryption achieve constant size. A performance analysis shows that the scheme can resist a variety of collusion attacks, and simulations show that it outperforms current schemes.
A non-linear multi-objective technique for hybrid peer-to-peer communication

This work proposes a strategy management technique based on hybrid peer-to-peer communication system. The main techniques used in the P2PC are: (i) Multi-objective optimization, (ii) Game theory technique, (iii) Non-linear geometric programming, and (iv) Intuitionistic fuzzy logic. Multi-objective optimization is used to design multiple non-linear objective functions. Game theory is used to model the conflicting strategies of the nodes using payoff matrices. Non-linear geometric programming is used to estimate uncertainty related parameters. Intuitionistic fuzzy logic is used to reduce the imprecise parameters of the nodes with the support of some objective functions. The set of stated techniques gives effective mathematical modelling to analyze the conflicting situation of the nodes. Therefore, this modelling helps to derive intelligent communication between source and destination nodes. Mathematical analysis and simulation results are used to validate the P2PC method. The simulation results are achieved in the Lingo optimization simulator using the existing methodologies in terms of some performance metrics.