Reviewerly/exHarmony huggingface dataset:
Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning

Purpose Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps. Design/methodology/approach This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results. Findings The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations. Originality/value This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.
Evaluation of MRI Denoising Methods Using Unsupervised Learning

In this paper we evaluate two unsupervised approaches to denoise Magnetic Resonance Images (MRI) in the complex image space using the raw information that k-space holds. The first method is based on Stein’s Unbiased Risk Estimator, while the second approach is based on a blindspot network, which limits the network’s receptive field. Both methods are tested on two different datasets, one containing real knee MRI and the other consists of synthetic brain MRI. These datasets contain information about the complex image space which will be used for denoising purposes. Both networks are compared against a state-of-the-art algorithm, Non-Local Means (NLM) using quantitative and qualitative measures. For most given metrics and qualitative measures, both networks outperformed NLM, and they prove to be reliable denoising methods.
A new adaptive Kalman filter for navigation systems of carrier-based aircraft

The features of carrier-based aircraft’s navigation systems during the approach and landing phases are investigated. A new adaptive Kalman filter with unknown state noise statistics is proposed to improve the accuracy of the INS/GNSS integrated navigation system. The adaptive filtering algorithm aims to estimate and adapt the unknown state noise covariance Q in high dynamic conditions, when the measurement noise covariance R is assumed to be known empirically in advance. The new adaptive Kalman filter based on the innovation sequence and pseudo-measurement vector approach makes it more effective to estimate and adapt Q. The simulation results and semi-physical experiments show that the application of the proposed adaptive Kalman filter can guarantee a higher estimation accuracy of the state variables.
An Enhanced Algorithm for Deinterleaving Mixed Radar Signals

An essentialtask of the electronic support measures system is to deinterleave the intercepted radar pulse stream. To improve the radars’ antijamming capability and adapt the radars to different application scenarios, the pulse signals emitted by various radars are becoming abundant, causing the pulse stream intercepted by the reconnaissancereceiver to becomemore and more complicated. Apart from fixed pulse repetition interval (PRI) signals, other PRI modulated signals, such as jitter PRI and staggered PRI, also appear in the intercepted radar pulse stream. The aforementioned pulse stream is defined as mixed radar signals in this article. The deinterleaving of mixed radar signals is an inevitable challenge in the field of electronic countermeasures. A novel algorithm for deinterleaving mixed radar signals is proposed in this article. It is flexible for deinterleaving different PRI modulated signals. A sequence retrieval method based on clustering ideas is also proposed in the algorithm, which enables different pulse sequences to be accurately separated. Simulation results show that the new algorithm has great PRI estimation accuracy for mixed radar signals in a large pulse missing rate.
A Hybrid Deep Model for Recognizing Arabic Handwritten Characters

Handwriting recognition for computer systems has been in research for a long time, with different researchers having an extensive variety of methods at their disposal.The problem is that most of these experiments are done in English, as it is the most spoken language in the world.But other languages such as Arabic, Mandarin, Spanish, French, and Russian also need research done on them since there are millions of people who speak them.In this work, recognizing and developing Arabic handwritten characters is proposed by cleaning the state-of-the-art Arabic dataset called Hijaa, developing Conventional Neural Network (CNN) with a hybrid model using Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classifiers.The CNN is used for feature extraction of the Arabic character images, which are then passed on to the Machine Learning classifiers.A recognition rate of up to 96.3% for 29classes is achieved, far surpassing the already state-of-the-art results of the Hijaa dataset.
Graph Convolutional Module for Temporal Action Localization in Videos

Temporal action localization, which requires a machine to recognize the location as well as the category of action instances in videos, has long been researched in computer vision. The main challenge of temporal action localization lies in that videos are usually long and untrimmed with diverse action contents involved. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e., proposals in two-stage methods and segments in one-stage methods) and then perform action recognition/regression on each of them individually, without explicitly exploiting their relations during learning. In this paper, we claim that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it. To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms. To be specific, we first construct a graph, where each action unit is represented as a node and their relations between two action units as an edge. Here, we use two types of relations, one for capturing the temporal connections between different action units, and the other one for characterizing their semantic relationship. Particularly for the temporal connections in two-stage methods, we further explore two different kinds of edges, one connecting the overlapping action units and the other one connecting surrounding but disjointed units. Upon the graph we built, we then apply graph convolutional networks (GCNs) to model the relations among different action units, which is able to learn more informative representations to enhance action localization. Experimental results show that our GCM consistently improves the performance of existing action localization methods, including two-stage methods (e.g., CBR [15] and R-C3D [47]) and one-stage methods (e.g., D-SSAD [22]), verifying the generality and effectiveness of our GCM. Moreover, with the aid of GCM, our approach significantly outperforms the state-of-the-art on THUMOS14 (50.9 percent versus 42.8 percent). Augmentation experiments on ActivityNet also verify the efficacy of modeling the relationships between action units. The source code and the pre-trained models are available at https://github.com/Alvin-Zeng/GCM.
BloCoV6: A blockchain-based 6G-assisted UAV contact tracing scheme for COVID-19 pandemic

In this article, the authors propose a scheme, BloCoV6, that integrates sixth-generation (6G)-assisted unmanned aerial vehicles (UAVs) and blockchain (BC) to monitor mass surveillance of persons in dense areas, and implement a trust-based contact-tracing ecosystem in BC. The scheme operates in two phases. In the first phase, based on the area density, and the number of users, UAVs swarms are mounted with thermal imaging sensors that monitor the body temperature of persons. The collected data are sent to ground stations in real-time, through 6G network services. Once, the images are analyzed, the details of potential COVID-19 patients are identified, and their travel and contact records are fetched and stored in BC. Then, in the second phase, the contact-tracing information is validated in BC. The proposed scheme is simulated for smart contracts (SC) functionalities, UAV observations, latency, spectral efficiency, and transaction and signing costs. The obtained results indicate the scheme viability. For example, 6G has a low latency of 330.8 milliseconds (ms), which outperforms 1200.1 ms in fifth-generation (5G) channels. The observed spectral efficiency of 6G channels is 5-10× higher than 5G, and the average signing and transaction cost is 3.473 seconds (s), and 6.873 s respectively, which outperforms the conventional schemes.
Faster Meta Update Strategy for Noise-Robust Deep Learning

It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this paper, we introduce a novel Faster Meta Update Strategy (FaMUS) to replace the most expensive step in the meta gradient computation with a faster layer-wise approximation. We empirically find that FaMUS yields not only a reasonably accurate but also a low-variance approximation of the meta gradient. We conduct extensive experiments to verify the proposed method on two tasks. We show our method is able to save two-thirds of the training time while still maintaining the comparable or achieving even better generalization performance. In particular, our method achieves the state-of-the-art performance on both synthetic and realistic noisy labels, and obtains promising performance on long-tailed recognition on standard benchmarks. Code are released at https://github.com/youjiangxu/FaMUS.
Comparison of the usability and flow experience of an exercise promotion virtual reality programme for different age groups

Population aging has become a common phenomenon worldwide, and various virtual reality (VR) applications are emerging. In this study, a VR exercise software programme was designed and compared with conventional exercise equipment in terms of the differences in usability and immersion perceived by middle-aged, older, and young adults. A VR software programme for dumbbell stretching exercise was designed, and 62 participants were invited. The participants were divided into a middle-aged and older adult group and a young adult group. Each participant used the designed software and physical dumbbells to complete three tasks. The results revealed that (1) software usability and immersion differed significantly between middle-aged and older adults and young adults; (2) the mean interface learnability score of the young adults was significantly higher than that of the middle-aged and older adults; and (3) the mean scores of the overall immersion and the four indicators for the VR exercise were higher than those for the physical exercise, regardless of whether the scores were examined among all participants or for the two age groups separately. The results of this study may provide designers within insights into the development VR software for older adults.
Principled approach to the selection of the embedding dimension of networks

Network embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension -- small enough to be efficient and large enough to be effective -- is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.
Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation

Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth bounding boxes, reducing the mask sensitivity to spatial location. Second, a video is directly divided into individual frames for frame-level instance segmentation, ignoring the temporal correlation between adjacent frames. To address these issues, we propose a simple yet effective one-stage video instance segmentation framework by spatial calibration and temporal fusion, namely STMask. To ensure spatial feature calibration with ground-truth bounding boxes, we first predict regressed bounding boxes around ground-truth bounding boxes, and extract features from them for frame-level instance segmentation. To further explore temporal correlation among video frames, we aggregate a temporal fusion module to infer instance masks from each frame to its adjacent frames, which helps our frame-work to handle challenging videos such as motion blur, partial occlusion and unusual object-to-camera poses. Experiments on the YouTube-VIS valid set show that the proposed STMask with ResNet-50/-101 backbone obtains 33.5 % / 36.8 % mask AP, while achieving 28.6 / 23.4 FPS on video instance segmentation. The code is released online https://github.com/MinghanLi/STMask.
Classification of cervical cancer using Deep Learning Algorithm

Cervical cancer is one of the most prevalent diseases in women ranking fourth in worldwide, mostly occurring in less-developed countries. This is perceived when certain vagaries occur in a woman's cervix. These cancer cells can also spread to other vital organs like lungs, liver and bladder which complicates the problem. Previous discoveries, tests and careful monitoring showed high levels of recovery rates at early detection of cancerous cells. But distinguishing cervical cells in Pap smear is demanding piece of work due to some constraints. Some of the constraint includes entanglement of the morphological changes in the structural parts of the cells. Although there are two methods to obtain the cells which are Colposcopy and pap-test but in reality, Pap-smear test are most favoured due to low cost and pain free diagnosis. Pap Smears are slides of cluster cells in which the cells vary in size, colour and morphology depending upon the degree of abnormality. Earlier studies have utilized both machine learning and deep learning methodologies, the prior one is not the most effective as it requires segmentation and obtaining hand crafted features which consumes crucial time. This paper presents deep learning classification methods applied on the SIPAKMED pap-smear image dataset to establish a reference point for the assessment of forthcoming classification techniques. With this approach highest classification accuracy of 94.89% was obtained with ResNet-152 architecture.
An Incremental Training on Deep Learning Face Recognition for M-Learning Online Exam Proctoring

The ability to provide an academic resource for a remote student has increased the use of m-learning in distance education. Online exams as a tool to measure the student's outcome need a proctoring method to detect cheats. Several methods had been proposed to fulfill these needs, from a no-proctoring exam to automatic online supervision. A visual verification during an online exam is required to verify a student took the exam, therefore a CNN-FR is used to do it. The problem that exists in face recognition is the system invariant against pose and lighting variations. In some proposed methods, an additional process such as image equalization and SURF features is executed to overcome the problems. In this paper, we proposed an incremental training process on face recognition training, so there will be no need to add another process so it will reduce the computation cost and time. To acquired high accuracy we've analyzed four different face detectors, which are Haar-cascade, LBP, MTCNN, and Yolo-face, as in face recognition a Facenet model was tested. The evaluation of the proposed method shows that a deep learning face detector has overcome the others, on the other hand, an incremental training of facenet model results in a smaller dataset size by 1% with a faster training time of 7% on Yolo-face face detector and 64% on MTCNN compared to batch training. The proposed method results in an equally high accuracy rate as in batch training (98%).
EdgeDR: An Online Mechanism Design for Demand Response in Edge Clouds

The computing frontier is moving from centralized mega datacenters towards distributed cloudlets at the network edge. We argue that cloudlets are well-suited for handling power demand response to help the grid maintain stability due to more flexible workload management attributed to their distributed nature. However, they also require computing demand response to avoid overload and maintain reliability. To this end, we propose a novel online market mechanism, EdgeDR, to achieve cost efficiency in edge demand response programs. At a high level, we observe that the cloudlet operator can dynamically switch on/off entire cloudlets to compensate for the energy reduction required by the power grid or provide enough computing resources to the edge service. We formulate a long-term social cost minimization problem and decompose it into a series of one-round procurement auctions. In each auction instance, we propose to let the cloudlet tenants bid with cost functions of their two-dimension service quality degradation tolerance, and let the cloudlet operator choose the service quality, manage the workload, and schedule the cloudlet activation status. In addition, we present a dynamic payment mechanism for the operator to balance the tradeoff between short-term profit and long-term benefit in more practical scenarios. Via rigorous analysis, we exhibit that our bidding policy is individually rational and truthful; our workload management algorithm has near-optimal performance in each auction; and our overall online algorithm achieves a provable competitive ratio. We further confirm the performance of our mechanism through extensive trace-driven simulations.
A Proposed Chatbot Framework for COVID-19

In recent years, chatbots have gained traction in a variety of fields, including health care, education, marketing, cultural heritage, support networks, entertainment, and many others. To manage the large number of user requests during pandemics, chatbots have become a must-have piece of equipment. In this paper, we present a smart chatbot system that can communicate with people and provide them with answers about the COVID-19. To tackle the popular role of question answering, our model used the pre-trained Google BERT language model. On top of the BERT, we add two architectural phases for the question-answering task. The first step is a text classification technique that employs the BERT Transformer to categorise text input into various categories based on the meaning of the words themselves. The actual application of the BERT model, as well as the query domain for answers, is the second step. Our proposed system is trained and tested on Stanford University’s SQuAD V2.0, a well-known question-answering dataset.
Experimental localisation of quantum entanglement through monitored classical mediator

Quantum entanglement is a form of correlation between quantum particles that cannot be increased via local operations and classical communication. It has therefore been proposed that an increment of quantum entanglement between probes that are interacting solely via a mediator implies non-classicality of the mediator. Indeed, under certain assumptions regarding the initial state, entanglement gain between the probes indicates quantum coherence in the mediator. Going beyond such assumptions, there exist other initial states which produce entanglement between the probes via only local interactions with a classical mediator. In this process the initial entanglement between any probe and the rest of the system "flows through" the classical mediator and gets localised between the probes. Here we theoretically characterise maximal entanglement gain via classical mediator and experimentally demonstrate, using liquid-state NMR spectroscopy, the optimal growth of quantum correlations between two nuclear spin qubits interacting through a mediator qubit in a classical state. We additionally monitor, i.e., dephase, the mediator in order to emphasise its classical character. Our results indicate the necessity of verifying features of the initial state if entanglement gain between the probes is used as a figure of merit for witnessing non-classical mediator. Such methods were proposed to have exemplary applications in quantum optomechanics, quantum biology and quantum gravity.
Segmentation and recognition of breast ultrasound images based on an expanded U-Net

This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application scenarios of semantic segmentation of breast ultrasound images, and adds dropout layers to the U-Net architecture to reduce the redundancy in texture details and prevent overfitting. The main innovation of this paper is proposing an expanded training approach to obtain an expanded of U-Net. The output map of the expanded U-Net can retain texture details and edge features of breast tumours. Using the grey-level probability labels to train the U-Net is faster than using ordinary labels. The average Dice coefficient (standard deviation) and the average IOU coefficient (standard deviation) are 90.5% (±0.02) and 82.7% (±0.02), respectively, when using the expanded training approach. The Dice coefficient of the expanded U-Net is 7.6 larger than that of a general U-Net, and the IOU coefficient of the expanded U-Net is 11 larger than that of the general U-Net. The context of breast ultrasound images can be extracted, and texture details and edge features of tumours can be retained by the expanded U-Net. Using an expanded U-Net can quickly and automatically achieve precise segmentation and multi-class recognition of breast ultrasound images.