Miracle-dz/UniFAR-FTUs huggingface dataset:
Problematic, severe asthma in children: a new concept and how to manage it

Correspondence to: Andrew Bush, Department of Paediatric Respiratory Medicine, Royal Brompton Hospital, Sydney Street, London SW3 6NP, UK. E. mail: a.bush@rbh.nthames. nhs.uk Most children with asthma respond to low doses of inhaled corticosteroids, but a few remain symptomatic despite being prescribed the routine usual asthma medications. Th e fi rst steps are to ensure that the diagnosis is correct and that the inhaled medications are being given regularly with an appropriately used device. If the children continue to be symptomatic, with any or all of chronic symptoms, acute exacerbations, the need for regular oral corticosteroids, or persistent airfl ow limitation, then they are considered to have problematic, severe asthma. Th e next step is to perform a detailed evaluation, including a nurse-lead home visit, to determine whether the child has diffi cult to treat asthma which improves if the basics are got right, or severe, therapy-resistant asthma; the latter group would be candidates for cytokine-specifi c therapies. If severe, therapy-resistant asthma is the likely issue, then a detailed invasive investigation is performed, including bronchoscopy, bronchoalveolar lavage and endobronchial biopsy, and trial of adherence with a single intramuscular injection of depot triamcinolone. Aft er detailed phenotyping, an individualised treatment plan is determined. Future work will determine the roles of proximal and distal infl ammation, as well as the relative importance of intramural (mucosal) and intraluminal infection. Th e stability of paediatric asthma phenotypes over time is more variable than those of adults, and the implications of a change of phenotype are yet to be determined.
Unraveling Metric Vector Spaces With Factorization for Recommendation

Unlike all prior work, in this article, we investigate the notion of “unraveling metric vector spaces,” i.e., deriving meaning and low-rank structure from distance or metric space. Our new model bridges two commonly adopted paradigms for recommendations—metric learning approaches and factorization-based models, distinguishing itself accordingly. More concretely, we show that factorizing a metric vector space can be surprisingly efficacious. All in all, our proposed method, factorized metric learning, is highly effective for two classic recommendation tasks, possessing the potential of displacing many popular choices as an extremely strong baseline. We have done experiments on a number of real-world datasets, which show that our model performs better than recent state of the art largely on the rating prediction and item ranking tasks.
Detection and Mitigation of Cyber-Attacks on AGC Systems of Low Inertia Power Grid

Automatic generation control (AGC) plays a salient role in modern power systems by maintaining grid frequency within acceptable limits. Since AGC is the only automatic feedback loop between cyber and physical infrastructures, it is greatly vulnerable to malicious attacks and hence, required urgent investigations. Attackers can falsify the sensor measurements employed for AGC operation causing service outage and infrastructural damage. In this article, a method is developed to detect and mitigate possible cyber-attack schemes such as false data injection and denial-of-service attacks targeting the AGC systems. The proposed method is termed as cyber-attack detection and mitigation platform (CDMP) and utilizes the forecasted data of area control error for identification and mitigation of attacks. The CDMP strategy involves three stages for optimal operation and identifies any arbitrary false data injected to the grid. For the investigation, three area system with multiple generations and distribution companies under bilateral trading is considered. The test model is also equipped with inertia emulation technique supported by battery management system. It is because the modern power system is advancing toward inverter-dominated system from synchronous-based system due to high penetration of renewables. The effectiveness of the proposed strategy is verified using rigorous simulation results.
Word bibliographic coupling: Another way to map science field and identify core references

This study investigates how to measure subject relationship based on bibliographic coupling strength. Since the 1960s, researchers use citation analysis methods to discover the relationship between different works and authors. However, how to apply citation‐based methods in measuring the relationships between various subjects remains unknown. We propose a novel method to measure relationships between subjects based on bibliographic coupling strengths. Our dataset is composed of 7,692 articles published in 10 core information science journals from 2008 to 2017. The result shows that our method provides another viewpoint of exploring the development of science. Furthermore, our method can identify the core works in different subjects and help to judge how similar different subjects are.
Non-interactive object extraction based on saliency detection

In traditional content-based image retrieval (CBIR), it is very important to segment the regions of interest for retrieval. In order to realize the non-interactive target region segmentation, this paper proposes the object segmentation based on significance region detection. The Grab Cut segmentation algorithm is initialized through the detection of the significance region, and the interactive operation is removed. The segmentation precision of the algorithm proposed in this paper is 89.51%.
Diffuse reflectance spectroscopy accurately identifies the pre-cortical zone to avoid impending pedicle screw breach in spinal fixation surgery.

Pedicle screw placement accuracy during spinal fixation surgery varies greatly and severe misplacement has been reported in 1-6.5% of screws. Diffuse reflectance (DR) spectroscopy has previously been shown to reliably discriminate between tissues in the human body. We postulate that it could be used to discriminate between cancellous and cortical bone. Therefore, the purpose of this study is to validate DR spectroscopy as a warning system to detect impending pedicle screw breach in a cadaveric surgical setting using typical clinical breach scenarios. DR spectroscopy was incorporated at the tip of an integrated pedicle screw and screw driver used for tissue probing during pedicle screw insertions on six cadavers. Measurements were collected in the wavelength range of 400-1600 nm and each insertion was planned to result in a breach. Measurements were labelled as cancellous, cortical or representing a pre-cortical zone (PCZ) in between, based on information from cone beam computed tomographies at corresponding positions. In addition, DR spectroscopy data was recorded after breach. Four typical pedicle breach types were performed, and a total of 45 pedicle breaches were recorded. For each breach direction, the technology was able to detect the transition of the screw tip from the cancellous bone to the PCZ (P < 0.001), to cortical bone (P < 0.001), and to a subsequent breach (P < 0.001). Using support vector machine (SVM) classification, breach could reliably be detected with a sensitivity of 98.3 % [94.3-100 %] and a specificity of 97.7 % [91.0-100 %]. We conclude that DR spectroscopy reliably identifies the area of transition from cancellous to cortical bone in typical breach scenarios and can warn the surgeon of impending pedicle breach, thereby resulting in safer spinal fixation surgeries.
Automatic Kidney and Tumor Segmentation with Hybrid Hierarchical Networks

Automatic segmentation of kidney and its tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis and treatment assessment. Kidney Tumor Segmentation Challenge (KiTS) provides a common platform for comparing diﬀerent automatic algorithms on abdominal CT images in tasks of 1) kidney segmentation and 2) kidney tumor segmentation . We participate this challenge by developing a fully automatic framework based on deep neural networks. By observing that clinicians usually contour organs and tumors in the axial view while evaluating the contours in 3D space, we adopt a 3-step hierarchical structure with hybrid 2D and 3D models. In the ﬁrst step, a simple 2D U-Net model is trained to obtain a quick but coarse segmentation of the kidney region on the entire 3D CT volume; then another 2D U-Net using residual blocks with channel-wise attention is applied to each kidney region for kidney and tumor segmentation. At last, the segmented tumor is reﬁned by a 3D model for ﬁnal tumor segmentation. Our framework was trained using the 210 challenge training cases provided by KiTS. By 5-fold evaluation, our method achieved an average Dice Similarity Coeﬃcient (DSC) of 0 . 970 on kidneys and 0 . 756 on kidney tumors, respectively.
Fast Video Object Segmentation by Pixel-Wise Feature Comparison

This final degree thesis tackles the task of One-Shot Video Object Segmentation, where multiple objects have to be separated from the background only having the ground truth masks for them in the very first frame. Their large pose and scale variations throughout the sequence, and the occlusions happening between them make this task very difficult to solve. Fast Video Object Segmentation by Pixel-Wise Feature Comparison goes a step further, and besides achieving comparable results with state-of-the-art methods, it works one order of magnitude faster than them, or even two in some cases.
A multi‐modal transportation data‐driven approach to identify urban functional zones: An exploration based on Hangzhou City, China

Recent urban studies have used human mobility data such as taxi trajectories and smartcard data as a complementary way to identify the social functions of land use. However, little work has been conducted to reveal how multi‐modal transportation data impact on this identification process. In our study, we propose a data‐driven approach that addresses the relationships between travel behavior and urban structure: first, multi‐modal transportation data are aggregated to extract explicit statistical features; then, topic modeling methods are applied to transform these explicit statistical features into latent semantic features; and finally, a classification method is used to identify functional zones with similar latent topic distributions. Two 10‐day‐long “big” datasets from the 2,370 bicycle stations of the public bicycle‐sharing system, and up to 9,992 taxi cabs within the core urban area of Hangzhou City, China, as well as point‐of‐interest data are tested to reveal the extent to which different travel modes contribute to the detection and understanding of urban land functions. Our results show that: (1) using latent semantic features delineated from the topic modeling process as the classification input outperforms approaches using explicit statistical features; (2) combining multi‐modal data visibly improves the accuracy and consistency of the identified functional zones; and (3) the proposed data‐driven approach is also capable of identifying mixed land use in the urban space. This work presents a novel attempt to uncover the hidden linkages between urban transportation patterns with urban land use and its functions.
A New Approach for Predicting Security Vulnerability Severity in Attack Prone Software Using Architecture and Repository Mined Change Metrics

Billions of dollars are lost every year to successful cyber attacks that are fundamentally enabled by software vulnerabilities. Modern cyber attacks increasingly threaten individuals, organizations, and governments, causing service disruption, inconvenience, and costly incident response. Given that such attacks are primarily enabled by software vulnerabilities, this work examines the efficacy of using change metrics, along with architectural burst and maintainability metrics, to predict modules and files that might be analyzed or tested further to excise vulnerabilities prior to release. The problem addressed by this research is the residual vulnerability problem, or vulnerabilities that evade detection and persist in released software. Many modern software projects are over a million lines of code, and composed of reused components of varying maturity. The sheer size of modern software, along with the reuse of existing open source modules, complicates the questions of where to look, and in what order to look, for residual vulnerabilities. Traditional code complexity metrics, along with newer frequency based churn metrics (mined from software repository change history), are selected specifically for their relevance to the residual vulnerability problem. We compare the performance of these complexity and churn metrics
BCEdge: Blockchain‐based resource management in D2D‐assisted mobile edge computing

In recent decades, newly emerging mobile applications and services are becoming increasingly resource hungry and computation intensive. Portable size mobile devices fall short in providing such services. Cloud computing has been a core computation technology to provide visualized resources in a scalable way for mobile services. However, the unpredictable network transmission latency makes cloud computing not efficient enough for time‐sensitive mobile services, which requires major changes in underlying computing platform. Mobile edge computing is widely known as one of the novel technologies has emerged in recent years to address this issue. For being impractical to construct huge edge cloud in network edge, edge clouds can still be overloaded in rush time. Offloading to near‐user facilities using device‐to‐device (D2D) or other technologies becomes an augmentation approach. However, how to manage the facilities/resources effectively become a new issue. In this paper, we proposed a resource management scheme named BCEdge based on blockchain in D2D‐assisted mobile edge computing. BCEdge is a reliable scheme that operates in a distributed way to relieve the load of edge clouds. We illustrate the advantages and technical details of BCEdge using flow charts and interaction charts. The experiment results validate the effectiveness of our scheme. Finally, we discuss the possible future extensions.
Comparative Study of Different Vehicle Models with Respect to Their Dynamic Behavior

Various simulation models are used extensively for the design and the optimisation of vehicle suspension systems, as well as the application of various control algorithms in them. The selection of the most suitable model for these purposes is never explained and many times unnecessary complexity is added in them. In this respect, an assessment regarding the accuracy of the most common vehicle models is conducted. Thus, four vehicle models with various configurations are compared in terms of accuracy. More specifically, both passive and semi active suspensions are employed to the models, while the effect of adding anti-roll bars and tire dampers is also investigated. The transient behaviour of the suspension system and the overall vehicle performance are assessed in terms of ride comfort, vehicle handling and road holding using different road excitations. The results illustrate the ability of lower accuracy models to cope well and that they should be preferred most of the times. Also, anti-roll bars and tire dampers should be neglected when the ride comfort is investigated, whereas they have to be included when simulations regarding road holding are conducted.
Accurate Recovery of Missing Network Measurement Data With Localized Tensor Completion

The inference of the network traffic data from partial measurements data becomes increasingly critical for various network engineering tasks. By exploiting the multi-dimensional data structure, tensor completion is a promising technique for more accurate missing data inference. However, existing tensor completion algorithms generally have the strong assumption that the tensor data have a global low-rank structure, and try to find a single and global model to fit the data of the whole tensor. In a practical network system, a subset of data may have stronger correlation. In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the stronger local correlation of data to form and recover sub-tensors each with a lower rank. Despite that it is promising to use local tensors, the finding of correlated entries faces two challenges, the data with adjacent indexes are not ones with higher correlation and it is difficult to find the similarity of data with missing tensor entries. To conquer the challenges, we propose several novel techniques: efficiently calculating the candidate anchor points based on locality-sensitive hash (LSH), building sub-tensors around properly selected anchor points, encoding factor matrices to facilitate the finding of similarity with missing entries, and similarity-aware local tensor completion and data fusion. We have done extensive experiments using real traffic traces. Our results demonstrate that LTC is very effective in increasing the tensor recovery accuracy without depending on specific tensor completion algorithms.
Sentiment Analysis in Health and Well-Being: Systematic Review

Background Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity. ” Objective This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. Methods Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. Results The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. Conclusions SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.
Hierarchical Identification With Pre-Processing

We study a two-stage identification problem with pre-processing to enable efficient data retrieval and reconstruction. In the enrollment phase, users’ data are stored into the database in two layers. In the identification phase an observer obtains an observation, which originates from an unknown user in the enrolled database through a memoryless channel. The observation is sent for processing in two stages. In the first stage, the observation is pre-processed, and the result is then used in combination with the stored first layer information in the database to output a list of compatible users to the second stage. Then the second step uses the information of users contained in the list from both layers and the original observation sequence to return the exact user identity and a corresponding reconstruction sequence. The rate-distortion regions are characterized for both discrete and Gaussian scenarios. Specifically, for a fixed list size and distortion level, the compression-identification trade-off in the Gaussian scenario results in three different operating cases characterized by three auxiliary functions. While the choice of the auxiliary random variable for the first layer information is essentially unchanged when the identification rate is varied, the second one is selected based on the dominant function within those three. Due to the presence of a mixture of discrete and continuous random variables, the proof for the Gaussian case is highly non-trivial, which makes a careful measure theoretic analysis necessary. In addition, we study a connection of the previous setting to a two observer identification and a related problem with a lower bound for the list size, where the latter is motivated from privacy concerns.
An Innovation of Interaction Method Based on Force Feedback in HMD System

With the development and wide application of virtual reality technology, the emerging virtual reality system brings better visual experience. In the field of scientific visualization, immersive visual experience and well-designed interactive method can enhance user experience. Since traditional 2D input devices such as mouse and keyboard cannot support direct 3D manipulation, they cannot be applied to virtual reality visualization. Moreover, basic VR interactive devices like controllers also have some problems such as low operation precision and the lack of sensory simulation and real-time feedback operation. In order to solve these problems, we propose a virtual reality interactive method based on force feedback to deal with the complex interactive operation of scientific data in 3D space. We also reported a user testing which compared our approach with traditional VR controllers in same tasks demonstrates that the proposed approach has higher accuracy and better user experience.
TORA Software for Modification of Distribution Network

PDAM Tirtanadi Medan is a water utility company. According to [1] PDAM Tirtanadi hasn’t built new installation for 10 years. It is predicted that distribution network still forming a circuit so that hasn’t been optimized [2]. The network will be optimal if a pipe installed isn’t forming a circuit without reducing the function of the pipe water. Modification network deleted circuit can be done by looking for minimum spanning tree. Many literatures contain several algorithms to solve minimum spanning tree problem like travelling salesman problem [3,4], Prim’s algorithm [5-7] and Kruskal’s algorithm [8]. In this work, the examination of a circuit done with a minimum spanning tree using TORA software.