Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyberbullying victimisation and internalising and externalising problems among adolescents: the moderating role of parent–child relationship and child's sex

Abstract Aims Previous research has found links between cyberbullying victimisation and internalising and externalising problems among adolescents. However, little is known about the factors that might moderate these relationships. Thus, the present study examined the relationships between cyberbullying victimisation and psychological distress, suicidality, self-rated poor mental health and substance use among adolescents, and tested whether parent–child relationship and child's sex would moderate these relationships. Methods Self-report data on experiences of cyberbullying victimisation, self-rated poor mental health, psychological distress, suicidality and substance use were derived from the 2013 Ontario Student Drug Use and Health Survey, a province-wide school-based survey of students in grades 7 through 12 aged 11–20 years (N = 5478). Logistic regression models adjusted for age, sex, ethnicity, subjective socioeconomic status and involvement in physical fighting, bullying victimisation and perpetration at school. Results Cyberbullying victimisation was associated with self-rated poor mental health (adjusted odds ratio (OR) 2.15; 95% confidence interval (CI) 1.64–2.81), psychological distress (OR 2.41; 95% CI 1.90–3.06), suicidal ideation (OR 2.38; 95% CI 1.83–3.08) and attempts (OR 2.07; 95% CI 1.27–3.38), smoking tobacco cigarette (OR 1.96; 95% CI 1.45–2.65), cannabis use (OR 1.82; 95% CI 1.32–2.51), and binge drinking (OR 1.44; 95% CI 1.03–2.02). The association between cyberbullying victimisation and psychological distress was modified by parent–child relationship and child's sex (three-way interaction term p < 0.05). The association between cyberbullying victimisation and psychological distress was much stronger among boys who have a negative relationship with their parents. Conclusions Findings suggest that cyberbullying victimisation is strongly associated with psychological distress in most adolescents with the exception of males who get along well with their parents. Further research using a longitudinal design is necessary to disentangle the interrelationship among child's sex, parent–child relationship, cyberbullying victimisation and mental health outcomes among adolescents in order to improve ongoing mental health prevention efforts.
Discontinuity Preserving Liver MR Registration With Three-Dimensional Active Contour Motion Segmentation

Objective: The sliding motion of the liver during respiration violates the homogeneous motion smoothness assumption in conventional nonrigid image registration and commonly results in compromised registration accuracy. This paper presents a novel approach, registration with three-dimensional (3D) active contour motion segmentation (RAMS), to improve registration accuracy with discontinuity-aware motion regularization. Methods: A Markov random field-based discrete optimization with dense displacement sampling and self-similarity context metric is used for registration, while a graph cuts-based 3D active contour approach is applied to segment the sliding interface. In the first registration pass, a mask-free L1 regularization on an image-derived minimum spanning tree is performed to allow motion discontinuity. Based on the motion field estimates, a coarse segmentation finds the motion boundaries. Next, based on magnetic resonance (MR) signal intensity, a fine segmentation aligns the motion boundaries with anatomical boundaries. In the second registration pass, smoothness constraints across the segmented sliding interface are removed by masked regularization on a minimum spanning forest and masked interpolation of the motion field. Results: For in vivo breath-hold abdominal MRI data, the motion masks calculated by RAMS are highly consistent with manual segmentations in terms of Dice similarity and bidirectional local distance measure. These automatically obtained masks are shown to substantially improve registration accuracy for both the proposed discrete registration as well as conventional continuous nonrigid algorithms. Conclusion/Significance: The presented results demonstrated the feasibility of automated segmentation of the respiratory sliding motion interface in liver MR images and the effectiveness of using the derived motion masks to preserve motion discontinuity.
Association of Cognitive Impairment and Dementia With Receipt of Cataract Surgery Among Community-Dwelling Medicare Beneficiaries

pendently, and the arithmetic mean of scores for each question was calculated. The mean (SD) score for each source is displayed. The significance level was not defined before data collection. Since the data were ordinal, the intersource differences were calculated with the Kruskal-Wallis analysis of variance and Mann-Whitney U test as a post hoc test. The interobserver reproducibility was measured with an intraclass correlation coefficient (Spearman correlation).
Author Gender Prediction in Russian Social Media Texts

Presently natural language processing for social media, in particular in the field of sentiment analysis and topic modeling, is gaining momentum for Russian texts. However, Slavic languages including Russian are still insufficiently explored in terms of computational sociolinguistics and authorship profiling (i.e. automatic identification of latent demographic features of online users such as gender, age, personality based on their texts). Being able to predict these features with a high degree of accuracy would certainly benefit marketing, psychological studies and security. In this paper we are attempting to build classifiers to predict gender of the author in Russian Twitter and Facebook texts and explore the effect of the cross-genre evaluation. We used the most common lemmas, a set of morphological and syntactic parameters as well as the part-ofspeech (POS) trigrams as features and multiple classifiers to train and test models. Twitter corpus was used for training, Facebook and test set of Twitter corpus were used for testing. The best models for Twitter were ExtraTreesClassifier and RandomForestClassifier with accuracy 0.72 and linearSVM for Facebook (0.71). The obtained results are comparable with stateof-the art results for Russian language for the texts of different genres.
Modular Networks: Learning to Decompose Neural Computation

Scaling model capacity has been vital in the success of deep learning. For a typical network, necessary compute resources and training time grow dramatically with model size. Conditional computation is a promising way to increase the number of parameters with a relatively small increase in resources. We propose a training algorithm that flexibly chooses neural modules based on the data to be processed. Both the decomposition and modules are learned end-to-end. In contrast to existing approaches, training does not rely on regularization to enforce diversity in module use. We apply modular networks both to image recognition and language modeling tasks, where we achieve superior performance compared to several baselines. Introspection reveals that modules specialize in interpretable contexts.
Ultra-Dense LEO: Integrating Terrestrial-Satellite Networks Into 5G and Beyond for Data Offloading

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra-dense low earth orbit (LEO) networks and the terrestrial networks to achieve efficient data offloading. In TSN, each ground user can access the network over C-band via a macro cell, a traditional small cell, or a LEO-backhauled small cell (LSC). Each LSC is then scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate and the number of accessed users while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite-based backhaul links. The optimization problem is then decomposed into two closely connected subproblems and solved by our proposed matching algorithms. The simulation results show that the integrated network significantly outperforms the non-integrated ones in terms of the sum data rate. The influence of the traffic load and LEO constellation on the system performance is also discussed.
Effect of a Program Combining Transitional Care and Long-term Self-management Support on Outcomes of Hospitalized Patients With Chronic Obstructive Pulmonary Disease: A Randomized Clinical Trial

Importance Patients hospitalized for chronic obstructive pulmonary disease (COPD) exacerbations have high rehospitalization rates and reduced quality of life. Objective To evaluate a hospital-initiated program that combined transition and long-term self-management support for patients hospitalized due to COPD and their family caregivers. Design, Setting, and Participants This single-site randomized clinical trial was conducted in Baltimore, Maryland, with 240 participants. Participants were patients hospitalized due to COPD, randomized to intervention or usual care, and followed up for 6 months after hospital discharge. Enrollment occurred from March 2015 to May 2016; follow-up ended in December 2016. Interventions The intervention (n = 120) was a comprehensive 3-month program to help patients and their family caregivers with long-term self-management of COPD. It was delivered by COPD nurses (nurses with special training on supporting patients with COPD using standardized tools). Usual care (n = 120) included transition support for 30 days after discharge to ensure adherence to discharge plan and connection to outpatient care. Main Outcomes and Measures The primary outcome was number of COPD-related acute care events (hospitalizations and emergency department visits) per participant at 6 months. The co-primary outcome was change in participants’ health-related quality of life measured by the St George’s Respiratory Questionnaire (SGRQ) at 6 months after discharge (score, 0 [best] to 100 [worst]; 4-point difference is clinically meaningful). Results Among 240 patients who were randomized (mean [SD] age, 64.9 [9.8] years; females, 61.7%), 203 (85%) completed the study. The mean (SD) baseline SGRQ score was 63.1 (19.9) in the intervention group and 62.6 (19.3) in the usual care group. The mean number of COPD-related acute care events per participant at 6 months was 0.72 (95% CI, 0.45-0.97) in the intervention group vs 1.40 (95% CI, 1.01-1.79) in the usual care group (difference, 0.68 [95% CI, 0.22 to 1.15]; P = .004). The mean change in participants’ SGRQ total score at 6 months was −1.53 in the intervention and +5.44 in the usual care group (adjusted difference, −6.69 [95% CI, −12.97 to −0.40]; P = .04). During the study period, there were 15 deaths (intervention: 7; usual care: 8) and 337 hospitalizations (intervention: 135; usual care: 202). Conclusions and Relevance In a single-site randomized clinical trial of patients hospitalized due to COPD, a 3-month program that combined transition and long-term self-management support resulted in significantly fewer COPD-related hospitalizations and emergency department visits and better health-related quality of life at 6 months after discharge. Further research is needed to evaluate this intervention in other settings. Trial Registration ClinicalTrials.gov Identifier: NCT02036294
Risk factors for childhood enteric infection in urban Maputo, Mozambique: A cross-sectional study

Background Enteric infections are common where public health infrastructure is lacking. This study assesses risk factors for a range of enteric infections among children living in low-income, unplanned communities of urban Maputo, Mozambique. Methods & findings We conducted a cross-sectional survey in 17 neighborhoods of Maputo to assess the prevalence of reported diarrheal illness and laboratory-confirmed enteric infections in children. We collected stool from children aged 1–48 months, independent of reported symptoms, for molecular detection of 15 common enteric pathogens by multiplex RT-PCR. We also collected survey and observational data related to water, sanitation, and hygiene (WASH) characteristics; other environmental factors; and social, economic, and demographic covariates. We analyzed stool from 759 children living in 425 household clusters (compounds) representing a range of environmental conditions. We detected ≥1 enteric pathogens in stool from most children (86%, 95% confidence interval (CI): 84–89%) though diarrheal symptoms were only reported for 16% (95% CI: 13–19%) of children with enteric infections and 13% (95% CI: 11–15%) of all children. Prevalence of any enteric infection was positively associated with age and ranged from 71% (95% CI: 64–77%) in children 1–11 months to 96% (95% CI: 93–98%) in children 24–48 months. We found poor sanitary conditions, such as presence of feces or soiled diapers around the compound, to be associated with higher risk of protozoan infections. Certain latrine features, including drop-hole covers and latrine walls, and presence of a water tap on the compound grounds were associated with a lower risk of bacterial and protozoan infections. Any breastfeeding was also associated with reduced risk of infection. Conclusions We found a high prevalence of enteric infections, primarily among children without diarrhea, and weak associations between bacterial and protozoan infections and environmental risk factors including WASH. Findings suggest that environmental health interventions to limit infections would need to be transformative given the high prevalence of enteric pathogen shedding and poor sanitary conditions observed. Trial registration ClinicalTrials.gov NCT02362932
Dice in the Black Box: User Experiences with an Inscrutable Algorithm

We demonstrate that users may be prone to place an inordinate amount of trust in black box algorithms that are framed as intelligent. We deploy an algorithm that purportedly assesses the positivity and negativity of a users' writing emotional writing. In actuality, the algorithm responds in a random fashion. We qualitatively examine the paths to trust that users followed while testing the system. In light of the ease with which users may trust systems exhibiting "intelligent behavior" we recommend corrective approaches.
Topological Area Graph Generation and its Application to Path Planning

Representing a scanned map of the real environment as a topological structure is an important research in robotics. %is currently an important research. Since topological representations of maps save a huge amount of map storage space and online computing time, they are widely used in fields such as path planning, map matching, and semantic mapping. We propose a novel topological map representation, the Area Graph, in which the vertices represent areas and edges represent passages. The Area Graph is developed from a pruned Voronoi Graph, the Topology Graph. The paper also presents path planning as one application for the Area Graph. For that, we derive a so-called Passage Graph from the Area Graph. Because our algorithm segments the map as a set of areas, the first experiment compares the results of the Area Graph with that of state-of-the-art segmentation approaches, which proved that our method effectively prevented over-segmentation. Then the second experiment shows the superiority of our method over the traditional A* planning algorithm.
Endoscopic treatment of biliary complications in left lobe living donor liver transplantation

Purpose Almost all of the publications regarding the treatment of biliary complications after liver transplantation are related to biliary complications after right lobe living donor liver transplantation (LDLT) and cadaveric liver transplantation (LT). The number of publications regarding endoscopic treatment of biliary complications after left lobe LDLT is negligible. In this study, we aimed to present the results of endoscopic treatments applied in the management of biliary complications developed in left-lobe duct-to-duct LDLT patients. Patients and methods Between 2008 and 2018, patients with duct-to-duct anastomosis who underwent ERCP due to biliary complication after left lobe LDLT were included in the study. Clinical data included patient demographics, indications for LDLT, duration till the first ERCP after LDLT, number of ERCP procedures, ERCP indications (stricture or leak), and treatment outcomes, including the need for percutaneous and surgical interventions. Results Among 13 patients who underwent ERCP, 2 (15%) had biliary leakage and 11 (8%) had an anastomotic stricture. Our endoscopic success rate was 100% in patients with biliary stricture. Despite the implementation of ERCP on two patients with leakage, they died due to the biliary complication. Conclusion Our results suggest that endoscopic treatment methods are successful in the management of biliary stricture complication in patients with left lobe LDLT and duct-to-duct anastomosis. Although our findings show that endoscopic treatments fail when there is a leakage after left lobe LDLT, there is a need for further studies that include more patients to reach a definite conclusion.
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives

Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDD CUP 99 dataset as a benchmark. Several studies question its usability while constructing a contemporary NIDS, due to the skewed response distribution, non-stationarity, and failure to incorporate modern attacks. In this paper, we compare the performance for KDD-99 alternatives when trained using classification models commonly found in literature: Neural Network, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes and K-Means. Applying the SMOTE oversampling technique and random undersampling, we create a balanced version of NSL-KDD and prove that skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers on minority classes (U2R and R2L), leading to possible security risks. We explore UNSW-NB15, a modern substitute to KDD-99 with greater uniformity of pattern distribution. We benchmark this dataset before and after SMOTE oversampling to observe the effect on minority performance. Our results indicate that classifiers trained on UNSW-NB15 match or better the Weighted F1-Score of those trained on NSL-KDD and KDD-99 in the binary case, thus advocating UNSW-NB15 as a modern substitute to these datasets.
Representation Learning from Orbit Sets for One-Shot Classification

The sample complexity of a learning task is increased by transformations that do not change class identity. Visual object recognition for example, i.e. the discrimination or categorization of distinct semantic classes, is affected by changes in viewpoint, scale, illumination or planar transformations. We introduce a weakly-supervised framework for learning robust and selective representations from sets of transforming examples (orbit sets). We train deep encoders that explicitly account for the equivalence up to transformations of orbit sets and show that the resulting encodings contract the intra-orbit distance and preserve identity either by preserving reconstruction or by increasing the inter-orbit distance. We explore a loss function that combines a discriminative term, and a reconstruction term that uses a decoder-encoder map to learn to rectify transformation-perturbed examples, and demonstrate the validity of the resulting embeddings for one-shot learning. Our results suggest that a suitable deﬁnition of orbit sets is a form of weak supervision that can be exploited to learn semantically relevant embeddings.
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits

We propose a bandit algorithm that explores by randomizing its history of rewards. Specifically, it pulls the arm with the highest mean reward in a non-parametric bootstrap sample of its history with pseudo rewards. We design the pseudo rewards such that the bootstrap mean is optimistic with a sufficiently high probability. We call our algorithm Giro, which stands for garbage in, reward out. We analyze Giro in a Bernoulli bandit and derive a $O(K \Delta^{-1} \log n)$ bound on its $n$-round regret, where $\Delta$ is the difference in the expected rewards of the optimal and the best suboptimal arms, and $K$ is the number of arms. The main advantage of our exploration design is that it easily generalizes to structured problems. To show this, we propose contextual Giro with an arbitrary reward generalization model. We evaluate Giro and its contextual variant on multiple synthetic and real-world problems, and observe that it performs well.
Cancer Risk from Exposure to Particulate Matter and Ozone According to Obesity and Health-Related Behaviors: A Nationwide Population-Based Cross-Sectional Study

Background: There is little evidence of an association between cancer risk and long-term exposure to ambient particulate matter <10 μm (PM10) and ozone (O3), according to obesity and health-related behaviors. Methods: In the 2012 Korean Community Health Survey, survey data on socioeconomic characteristics, health-related behaviors, and previous cancer history were collected from 100,867 participants. Daily average concentrations of PM10 and O3 (2003–2012) were obtained from the Korean Air Pollutants Emission Service. The cancer risks for interquartile increases in PM10 and O3 were evaluated using multiple logistic regression and were stratified by age, sex, obesity, and health-related behaviors. Results: Increased cancer risk was found among obese subjects aged ≥50 years after adjusting for confounding factors [PM10: ≥60 years: OR 1.34, 95% confidence interval (CI) 1.03–1.74; 50–60 years: OR 1.40, CI 1.01–1.96; O3: ≥60 years: OR 1.12, CI 1.04–1.20; 50–60 years: OR 1.20, CI 1.08–1.33]. However, we did not observe similar trends in the nonobese subjects. Among obese subjects aged ≥50 who had been exposed to PM10, men, ever smokers, and inactive subjects were at increased cancer risk. Regarding O3, the cancer risk was significantly higher among obese adults >50 years old, regardless of sex or health-related behaviors. Conclusions: Long-term exposure to PM10 and O3 was found to increase cancer risk. In particular, the risk differed according to obesity status, age, sex, and health-related behaviors. Impact: The effect of air pollution on cancer risk was compounded by obesity, smoking, and physical inactivity among subjects over 50 years old.