Reviewerly/exHarmony huggingface dataset:
College English cross-cultural teaching based on cloud computing MOOC platform and artificial intelligence

College English cross-cultural teaching has changed from offline to online teaching. Under the impetus of MOOC teaching mode, college English cross-cultural teaching online teaching has exposed problems such as insufficient intelligence and poor online teaching effects. In order to improve the efficiency of college English cross-cultural teaching, based on cloud computing technology and artificial intelligence technology, this article improves and analyzes traditional MOOC, improves traditional algorithms according to the actual needs of MOOC teaching, and proposes a new improved model. Moreover, this article sets up functional modules through requirements analysis. In addition, according to actual teaching, this paper designs control experiments to verify and analyze the model performance from experimental teaching and the effect of attracting students’ online learning. The research results show that the model proposed in this paper has good performance and can effectively improve the efficiency of English cross-cultural teaching.
HDNet: Human Depth Estimation for Multi-person Camera-Space Localization

Current works on multi-person 3D pose estimation mainly focus on the estimation of the 3D joint locations relative to the root joint and ignore the absolute locations of each pose. In this paper, we propose the Human Depth Estimation Network (HDNet), an end-to-end framework for absolute root joint localization in the camera coordinate space. Our HDNet first estimates the 2D human pose with heatmaps of the joints. These estimated heatmaps serve as attention masks for pooling features from image regions corresponding to the target person. A skeleton-based Graph Neural Network (GNN) is utilized to propagate features among joints. We formulate the target depth regression as a bin index estimation problem, which can be transformed with a soft-argmax operation from the classification output of our HDNet. We evaluate our HDNet on the root joint localization and root-relative 3D pose estimation tasks with two benchmark datasets, i.e., Human3.6M and MuPoTS-3D. The experimental results show that we outperform the previous state-of-the-art consistently under multiple evaluation metrics. Our source code is available at: https://github.com/jiahaoLjh/HumanDepth .
Open-source Serverless Architectures: an Evaluation of Apache OpenWhisk

The serverless computing paradigm ushers in new concepts for running applications and services in the cloud. Currently, commercial solutions dominate the market, though open-source solutions do exist. As a consequence of this, there is little research detailing how well the different open-source solutions perform. In this paper, one such open-source solution, Apache OpenWhisk, is investigated to shed light on the capabilities and limitations inherent of such serverless computing architecture, and principally to provide further research on this particular solution's performance. This is accomplished through an extensive evaluation of OpenWhisk, involving a variety of experiments and benchmarks.
DSmT-based three-layer method using multi-classifier to detect faults in hydraulic systems

Fault identification in hydraulic valves is essential in maintaining the reliability and security of hydraulic systems. Due to the nonlinear characteristics of hydraulic systems under noisy working conditions, it is difficult to extract fault features from vibration signals collected from the surface of the valve body. Therefore, a DSmT-based three-layer method using multi-classifier is proposed to detect multiple faults occurred in hydraulic valves. Firstly, the raw signals are personalized to construct the training samples and the unknown testing samples. Secondly, a three-layer structure of the hybrid model called the layered hybrid model is constructed, which is suitable for hydraulic valves to detect the faults of different fault groups (including coil fatigue in the actuator and the abrasion inside the valve) and improve the diagnosis accuracy obviously. Finally, classification methods are selected to classify fault groups in the first two layers, and then the fault types are identified in the third layer by the fusion results using the Dezert-Smarandache Theory (DSmT). Experimental investigations are performed to validate the performance of the present method using a hydraulic valve (solenoid controlled pilot operated directional valve) controlled the hydraulic test rig. The results show that the average accuracy of detecting twelve types of faults is about 98.1%, which are better than those using other methods. It is expected that the present DSmT-based three-layer method using multi-classifier can be applied to more complex hydraulic systems.
A novel data clustering approach based on whale optimization algorithm

Abstract Data clustering is an important technique of data mining in which the objective is to partition N data objects into K clusters that minimize the sum of intra‐cluster distances between each data object to its nearest centroid. This is an optimization problem, and various optimization algorithms have been suggested for capturing the position vectors of optimal centroids. However, in these approaches, the problem of local entrapment is very common due to weak exploration mechanism. In this paper, a novel approach based on a whale optimization algorithm (WOA) is suggested for data clustering. The performance of the suggested approach is validated using 14 benchmark datasets of the UCI machine learning repository. The experimental results and various statistical tests have justified the efficacy of the suggested approach.
The future of university education: Examination, transcript, and certificate system using blockchain

Abstract Blockchain is slowly but surely changing the world. Cryptocurrencies are having a profound effect on the economic landscape. With the advent of COVID‐19, the education sector, just like other sectors, need a complete reinvention or redesign to continue producing graduates that would be able to effectively contribute to an ever‐changing world. Very often, the administrative burden of managing the award of academic qualifications is ignored. With a globalised world and given the actual context of COVID‐19, education is no longer seen as being carried out within the four walls of the classroom. The future of University education lies within the ability to pool resources from different places and to encourage cross‐university collaboration. This can also be seen as the ability to assemble the different building blocks so that the learners are able to acquire the necessary skills and knowledge. This study focuses on the design and implementation of an examination, transcript and certificate system using blockchain. It is meant to provide a completely transparent and effective examination and awards system that can be used to replace the traditional one in place. What has also proposed in this study is a novel light blockchain system composed of a number of modules, which have been built from scratch. With its decentralised approach, the proposed system proved to be very much helpful. Other features, such as security, immutability, availability, and anonymity, of the system are also discussed and yielded highly encouraging results.
A BERT-Based Named Entity Recognition in Chinese Electronic Medical Record

Named entity recognition, aiming at identifying and classifying named entity mentioned in the structured or unstructured text, is a fundamental subtask for information extraction in natural language processing (NLP). With the development of electronic medical records, obtaining the key and effective information in electronic document through named entity identification has become an increasingly popular research direction. In this article, we adapt a recently introduced pre-trained language model BERT for named entity recognition in electronic medical records to solve the problem of missing context information and we add an extra mechanism to capture the relationship between words. Based on this, (1) the entities can be represented by sentence-level vector, with the forward as well as backward information of the sentence, which can be directly used by downstream tasks; (2) the model acquires the representation of word in context and learn the potential relation between words to decrease the influence of inconsistent entity markup problem of a text. We conduct experiments an electronic medical record dataset proposed by China Conference on Knowledge Graph and Semantic Computing in 2019. The experimental result shows that our proposed method has an improvement compared with the traditional methods.
An improved LSHADE-RSP algorithm with the Cauchy perturbation: iLSHADE-RSP

A new method for improving the optimization performance of a state-of-the-art differential evolution (DE) variant is proposed in this paper. The technique can increase the exploration by adopting the long-tailed property of the Cauchy distribution, which helps the algorithm generate a trial vector with great diversity. Compared to the previous approaches, the proposed approach perturbs a target vector instead of a mutant vector based on a jumping rate. We applied the proposed approach to LSHADE-RSP ranked second place in the CEC 2018 competition on single objective real-valued optimization. A set of 30 different and difficult optimization problems is used to evaluate the optimization performance of the improved LSHADE-RSP. Our experimental results verify that the improved LSHADE-RSP significantly outperformed not only its predecessor LSHADE-RSP but also several cutting-edge DE variants in terms of convergence speed and solution accuracy.
A Quantum Spatial Graph Convolutional Network for Text Classification

The data generated from non-Euclidean domains and its graphical representation (with complex-relationship object interdependence) applications has observed an exponential growth.The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms.In this study, we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data.Additionally, the quantum information theory has been applied through Graph Neural Networks (GNNs) to generate Riemannian metrics in closed-form of several graph layers.In further, to pre-process the adjacency matrix of graphs, a new formulation is established to incorporate high order proximities.The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network (GCN), particularly, the information loss and imprecise information representation with acceptable computational overhead.Moreover, the proposed Quantum Graph Convolutional Network (QGCN) has significantly strengthened the GCN on semi-supervised node classification tasks.In parallel, it expands the generalization process with a significant difference by making small random perturbations ÁG of the graph during the training process.The evaluation results are provided on three benchmark datasets, including Citeseer, Cora, and PubMed, that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.
An optima-identified framework with brain storm optimization for multimodal optimization problems

Locating multiple optima and maintaining these identified solutions are two crucial issues in solving multimodal optimization problems (MMOPs). To address these two challenges, an optima-identified framework (OIF) combined with brain storm optimization (BSO) algorithm is proposed in this paper, which can identify global optimal solutions found during the search process, and maintain these optima until the end of the run. First, a max-fitness clustering method (MCM) is applied to form different clusters and each cluster center is likely to become an extreme point. Then, a modified disruption strategy (MDS) is devised to distinguish and identify potential optima among these cluster centers. Finally, we introduce two kinds of redistribution strategies (RS) to make the most of the individuals in those clusters whose cluster centers have been identified as global optima. We validate the effectiveness of the OIF and compare the proposed OIF-BSO algorithm with other state-of-the-art multimodal optimization algorithms. The results indicate that our framework is feasible to maintain and utilize cluster centers over the course of search, and the proposed algorithm can outperform other algorithms.
An Evolutionary-Optimized Surgical Path Planner for a Programmable Bevel-Tip Needle

Path planning algorithms for steerable needles in medical applications must guarantee the anatomical obstacle avoidance, reduce the insertion length, and ensure the compliance with the needle kinematics. The majority of the solutions from the literature focus either on fast computation or on path optimality, the former at the expense of suboptimal paths, the latter by making unbearable the computation in case of a high-dimensional workspace. In this article, we implement a three-dimensional path planner for neurosurgical applications, which keeps the computational cost consistent with standard preoperative planning algorithms and fine-tunes the estimated pathways in accordance to multiple optimization objectives. From a user-defined entry point, our method confines a sample-based path search within a subsection of the original workspace considering the degree of curvature admitted by the needle. An evolutionary optimization procedure is used to maximize the obstacle avoidance and reduce the insertion length. The pool of optimized solutions is examined through a cost function to determine the best path. Simulations on one dataset showed the ability of the planner to save time and overcome the state of the art in terms of obstacle avoidance, insertion length, and probability of failure, proving this algorithm as a valid planning method for complex environments.
Exploring groups of opinion spam using sentiment analysis guided by nominated topics

Currently, it is common to see untruthful opinions (also known as review spam, fraud or shilling attack) that resemble each other explicitly or implicitly across multiple business-to-customer websites or opinion sharing communities. Unfortunately, these fake recommendations can be fabricated by individual spammers or results of a manipulation campaign. Considering its severe harmfulness in influencing a product's reputation, grouped spam is more urgent to detect than individual fraud. Most state-of-the-art techniques of labeling grouped spam, e.g., Frequent Itemset Mining (FIM) or Latent Dirichlet Allocation (LDA), are completely unsupervised and incapable of making good use of officially recommended topics, such as appearance, speed and standby are three suggested aspects along a cell phone product in JD.com. In this paper, we introduce a novel approach based on aspect-oriented sentiment mining that can identify spam groups supported by nominated topics. Experiments show that our method is effective and outperforms several state-of-the-art solutions with statistical significance on two metrics, content duplication and burstiness of time.
Long-range Attention Network for Multi-View Stereo

Learning-based multi-view stereo (MVS) has recently gained great popularity, which can efficiently infer depth map and reconstruct fine-grained scene geometry. Previous methods calculate the variance of the corresponding pixel pairs to determine whether they are matched mostly based on the pixel-wise measure, which fails to consider the interdependence among pixels and is ineffective on the matching of texture-less or occluded regions. These false matching problems challenge MVS and result in its most failure cases. To address the issues, we introduce a Long-range Attention Network (LANet) to selectively aggregate reference features to each position to capture the long-range interdependence across the entire space. As a result, similar features relate to each other regardless of their distance, propagating more guiding information for the effective match. Furthermore, we introduce a new loss to supervise the intermediate probability volume by constraining its distribution reasonably centered at the true depth. Extensive experiments on large-scale DTU dataset demonstrate that the proposed LANet achieves the new state-of-the-art performance, outperforming previous methods by a large margin. Our method is generic and also achieves comparable results on outdoor Tanks and Temples dataset without any fine-tuning, which validates our method's generalization ability.
SiamATL: Online Update of Siamese Tracking Network via Attentional Transfer Learning

Visual object tracking with semantic deep features has recently attracted much attention in computer vision. Especially, Siamese trackers, which aim to learn a decision making-based similarity evaluation, are widely utilized in the tracking community. However, the online updating of the Siamese fashion is still a tricky issue due to the limitation, which is a tradeoff between model adaption and degradation. To address such an issue, in this article, we propose a novel attentional transfer learning-based Siamese network (SiamATL), which fully exploits the previous knowledge to inspire the current tracker learning in the decision-making module. First, we explicitly model the template and surroundings by using an attentional online update strategy to avoid template pollution. Then, we introduce an instance-transfer discriminative correlation filter (ITDCF) to enhance the distinguishing ability of the tracker. Finally, we suggest a mutual compensation mechanism that integrates cross-correlation matching and ITDCF detection into the decision-making subnetwork to achieve online tracking. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art tracking algorithms on multiple large-scale tracking datasets.
Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola-Jones Model

One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions.Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola-James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images, while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast, the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results, we achieved 95.43% accuracy, 92.20% sensitivity, and 97.5% specificity when we used the breast cancer data set.For the ovarian tumor data set, we achieved 94.84% accuracy, 96.96% sensitivity, and 90.32% specificity.
Configuration of Mobile Learning Tools to Support Basic Physical Assessment in Nursing Education: Longitudinal Participatory Design Approach

Background As many students in higher education are skilled users of mobile technology, mobile learning (mLearning) can be a promising educational strategy to enhance their learning experience. mLearning might also be well suited for nursing students as they navigate between multiple learning contexts in their educational curriculum. As an educational strategy, mLearning may also reduce challenges caused by the theory-practice gap in nursing by supporting skills and knowledge transfer between the university and clinical settings. As the introduction of basic physical assessment skills (B-PASs) into Norwegian bachelor’s degree education in nursing occurred quite recently, there is a lack of competence in supervision and teaching in both university and clinical settings. As such, mLearning appears to be a good strategy to support student B-PAS learning and knowledge transfer across learning contexts. Objective This study aims to explore and elicit the perspectives of students regarding the way in which a selection of digital learning resources supports B-PAS learning and application in clinical rotation, which of the selected digital learning resources are beneficial to include in a suite of mLearning tools, and how the selected digital learning resources could support the transfer of skills and knowledge from the academic to clinical context. Methods We used a longitudinal participatory design approach to co-design a suite of mLearning tools. The co-design processes took place in several workshops (WSs) over a period of 3 months: 2 WSs with first-year students (n=6), 3 WSs with second-year students (n=6), and 3 WSs with third-year students (n=8). The students evaluated several digital learning resources in both academic and clinical contexts. The digital learning resources included digital simulation with virtual patients, massive open online courses, and multimedia learning material. In the co-design WS, the potential and benefits of these digital learning resources for the learning and application of B-PASs were explored. Results The students reported that the digital learning resources stimulated learning in 7 different ways. They also emphasized the importance of including all selected and tested digital learning resources. Moreover, students supported the inclusion of additional learning material, such as multiple-choice tests and written assignments, aimed at providing feedback and contributing to knowledge development. Conclusions The co-design processes and collaboration with the nursing students provided insight into how a suite of mLearning tools may support the learning and application of B-PASs and human bioscience knowledge in clinical rotation. From the students’ perspective, one of the strengths of the suite of mLearning tools was the range of content, as this met a broader range of student learning preferences regarding learning B-PASs. The suite of mLearning tools contributes to and supports skills training and knowledge transfer between multiple learning contexts.
A new interval type-2 fuzzy logic system under dynamic environment: Application to financial investment

This paper proposes a new interval type-2 fuzzy logic system (IT2 FLS) for financial investment with time-varying parameters adaptive to real-time data streams by using an on-line learning method based on a state-space framework. Particularly, our state-space approach regards the parameters of IT2 FLSs as state variables to sequentially learn by Bayesian filtering algorithms under dynamic environments, where time-series data are continuously observed with occasional structural changes. Moreover, our proposal is effective for financial investment, which often involves various practical complex constraints, because general state-space model makes it possible to flexibly deal with non-linearities. In our empirical experiment with time-series data of global financial assets, our approach is applied to on-line parameter learning of type-1 and type-2 FLSs for portfolio decision making. As a result, it is shown that the IT2 FLS holds its advantage against the type-1 FLS, even though both of the type-1 and type-2 models have the adaptive time-varying parameters, which is an unexplored topic for empirical studies of this area.