Miracle-dz/UniFAR-FTUs huggingface dataset:
User anchor link identification based on integrated similarity decision in multiple heterogeneous social networks

The continuous development of social networks has led to the emergence of various forms of social networks, and people are willing to participate in multiple social networks to enjoy new services. Once a user joins a new social network, a new connection relationship called a user anchor link is formed between users in the two networks. Identifying user anchor links makes sense for many cross-network applications, such as using multiple network information for link recommendations and community discovery. This paper studies the identification problem of user anchor link in multiple heterogeneous social network, and pro-poses a user anchor link identification algorithm in multiple heterogeneous social network based on integrated similarity decision. The algorithm includes two components. One component is to utilize the users’ individual natural attributes and behaviors to decide their similarity; the other component is to decide the user similarity through the improved user friend relationship calculation. Finally, the results of the above two components are integrated to realize the similarity judgment between users, and then identify the users anchor link relationship. Relevant experiments carried out on two heterogeneous social networks show that the pro-posed algorithm outperform other commonly-used baselines on user anchor link identification.
Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

The detection and removal of malicious social bots in social networks has become an area of interest in industry and academia. The widely used bot detection method based on machine learning leads to an imbalance in the number of samples in different categories. Classifier bias leads to a low detection rate of minority samples. Therefore, we propose an improved conditional generative adversarial network (improved CGAN) to extend imbalanced data sets before applying training classifiers to improve the detection accuracy of social bots. To generate an auxiliary condition, we propose a modified clustering algorithm, namely, the Gaussian kernel density peak clustering algorithm (GKDPCA), which avoids the generation of data-augmentation noise and eliminates imbalances between and within social bot class distributions. Furthermore, we improve the CGAN convergence judgment condition by introducing the Wasserstein distance with a gradient penalty, which addresses the model collapse and gradient disappearance in the traditional CGAN. Three common oversampling algorithms are compared in experiments. The effects of the imbalance degree and the expansion ratio of the original data on oversampling are studied, and the improved CGAN performs better than the others. Experimental results comparing with three common oversampling algorithms show that the improved CGAN achieves the higher evaluation scores in terms of F1-score, G-mean and AUC.
Fractal Shape Dual-Band EBG integrated Textile Antenna

In paper, a modified design and response of fractal shape dual-band Electromagnetic bandgap (EBG) integrated textile antenna is detailed. Designed antenna works on the GSM and ISM bands with frequency 1.8 GHz and 2.45 GHz respectively. Antenna prototype is made up of common clothing fabric like jean and adhesive copper tape. The paper contains software simulation on Ansoft HFSS (high-frequency structure simulator) and hardware model of the antenna with EBG and without EBG. Required results are found even after changes in the structure of EBG. The design of monopole antenna, bandgap structure and their integrated is presented. The $3 \times 3$ bandgap array structure is used to reduces the back radiation into human body over 16 dB.
Reinforcement Learning Based Interference Control Scheme in Heterogeneous Networks

Heterogeneous networks (HetNet) guarantee higher throughput and lower latency than traditional homogeneous network environments. In order to guarantee such performance, interference control between the macro base station (MBS) and the small cell base station (SBS) have to be effectively performed. However, the existing interference control scheme has limitations in controlling interference for rapidly increasing SBS and UEs. In order to solve this problem, we introduce interference control and handover scheme with reinforcement learning in HetNet. Each BS learns transmission power, activation pattern and bias values for optimal network performance in HetNet. We introduce HetNet technologies incorporating various reinforcement learning models and introduce research areas that will be conducted in the future.
Control of a Silicone Soft Tripod Robot via Uncertainty Compensation

Soft robot is an emergent research field which has variant promising applications, and the control of such robots is still challenging. Unlike using different techniques (such as Beam theory, Cosserat theory or high dimensional finite-element method) to model the dynamics of soft robots, this letter introduces a simplified nominal model with uncertainty to describe its dynamic behavior. The link between this simple model and the finite-element method has been established, and a robust controller is proposed, by compensating the uncertainty which is estimated in a finite time by applying different types of estimators. The experiments have been made for different scenarios, and the corresponding results show the efficiency of the proposed method.
Predicting the Distribution of Indicator Taxa of Vulnerable Marine Ecosystems in the Arctic and Sub-arctic Waters of the Nordic Seas

In the deep waters of the Nordic Seas and adjacent areas, several benthic habitats such as cold-water coral reefs, coral gardens, and deep-sea sponge aggregations have been classified as vulnerable marine ecosystems (VMEs), due to their uniqueness, limited spatial extent, physical fragility, and slow recovery rate. In the last decade observations carried out by habitat mapping programmes in Norway, Iceland, and more recently in the Faroe Islands have substantially increased knowledge on the distribution of VMEs in the Nordic Seas. Nevertheless, large areas have not been explored due to the cost and logistics of obtaining observations in the deep-sea. Species distribution models can be used to predict the distribution of VMEs and their indicator species. Here we present the predicted distribution of 44 VME indicator taxa including 20 sponges, 17 cold-water corals, and 7 seapens in the Nordic Seas based on data compiled and models developed by the NovasArc project (2016–2018). Models for 44 VME indicator species were developed using the maximum entropy algorithm MaxEnt, using an extensive database compiled from habitat mapping surveys, by-catch data from bottom fish surveys, and records from reports and peer reviewed publications. Modeled distributions showed good agreement with observations. Niche overlap measures were used to identify seven groups and four subgroups of VME indicator taxa that co-occur. These were consistent with the species composition of known biotopes in the study area. A VME Index that combine the predictions for all VME indicator species was computed to identify particularly valuable and vulnerable ecosystems that should be targets of further exploration and conservation efforts. Such areas were identified at shelf break and slope off Iceland, the Faroe Islands, and central Norway, and the continental shelf off southern Greenland. The predicted distribution of VMEs in Arctic and sub-Arctic waters allows for the evaluation of interactions with fisheries and other anthropogenic activities and provides an important input for managers.
A benchmark for rotation extraction between two images in visual navigation

When using camera as an aiding sensor, extraction of accurate pose measurement in the form of translation and rotation is instrumental to the navigation solution. This paper shows the experimental results of accurate angle extraction between a sequence of images taken from a calibrated camera while keeping the scale constant. In this work a complete mathematical model for rotation extraction is discussed comprising of feature extraction and matching, epipolar geometry constraints, structure from motion (SFM), etc. As far as the author’s knowledge, such a benchmarking for rotation extraction has not been previously reported and hence this algorithm can be used as a beginning point for new practitioners in the field of visual navigation. This benchmarking algorithm is also provided as an open-source toolbox for MATLAB available at https://github.com/maazmb/rotation_extraction. git.
Compressive Sampling and Reconstruction of ECG Signal for Manned Spaceflight Applications

Electrocardiogram (ECG) is a vital signal which represents the state of health of astronauts in a manned spaceflight mission and hence must be continuously acquired and transmitted throughout the mission. Telemetry bandwidth is a premium resource in such applications. Traditional method of sampling at Nyquist rate is highly bandwidth inefficient. Hence it is advantageous to use Compressive sensing (CS) technique to optimize data at measurement point itself. Conventional CS techniques employ computationally intensive measurement matrices which are not hardware efficient for both acquisition as well as recovery. In this work, a hardware efficient scheme with use of a sparse binary measurement matrix is proposed. The signal is recovered from the compressive measurement using a constraint function based on inverted Laplace distribution function. Gradient decent method is used to recursively recover the original ECG signal from the compressive measurements in an efficient manner. Apart from this, a projection scheme is also proposed to minimize the recovery error. The proposed scheme was extensively evaluated with different ECG samples with different compression ratios. Finally, the proposed scheme was benchmarked with Approximated L0 norm based method and it is found to perform more efficiently in compressively sensing and recovery of ECG signals.
Community Characteristics, Victimization, and Psychological Adjustment Among School-Aged Adopted Children With Lesbian, Gay, and Heterosexual Parents

Little research has examined victimization among school-aged children raised in lesbian/gay (LG) parent households and almost no work has attended to the school and community contexts that may impact their victimization risk. This study examined predictors of parent-reported child victimization and child adjustment, and parent responses to victimization, in 43 two-mother, 37 two-father, and 56 mother–father families, with adopted children (median age = 8.6 years). Predictors included parent (sexual orientation), school (climate, public versus private) and community (urbanicity, percentage voted Democrat) factors, with parent and child demographics included as controls. A total of 47% of parents reported one or more child victimization experiences in the past year; there were no differences by family type. An exploratory interaction between family type and urbanicity indicated that in large urban areas, children with LG parents were predicted to experience less victimization than children with heterosexual parents; in more rural regions, children with LG parents were predicted to experience more victimization than children with heterosexual parents. School climate was related to victimization: Parents who reported more negative school climate reported more child victimization. Children with higher levels of parent-reported victimization had higher levels of parent-reported internalizing and externalizing symptoms. In large urban areas, children with LG parents were predicted to have fewer internalizing symptoms than children with heterosexual parents; in more rural areas, children with LG parents were predicted to have more internalizing symptoms than children with heterosexual parents. Regarding parents’ responses to victimization, LG parents were more likely to talk to school administrators, their children, and the bully, compared to heterosexual parents.
Fuzzy Logic Management of Hybrid Energy Storage System

Global energy challenges, increasing environmental concern, increasing power demand have led to the wide adoption of renewable energy sources like the photovoltaic systems. Standalone operation is a difficult task due to its intermittent nature. Here comes the use of an energy storage device which acts as an energy buffer. Among the energy storage devices, batteries are often preferred. Stress on battery increases when sudden fluctuations in the load profile appears. It leads to the deep discharge of batteries which damages as well as shortens its lifetime. In order to remove the stress from the batteries, supercapacitors (SC) are incorporated to form a hybrid energy storage system. Supercapacitors respond quickly to the instantaneous power demand. Hysteresis control and fuzzy logic energy management strategy have been introduced. The overall system is developed in MATLAB/Simulink environment. Simulation results prove the validity of the proposed strategy.
Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task

Twenty-three young adults (4 Females, 25.13 ± 3.72 years) performed an intermittent maximal handgrip force task using their dominant hand for 20 min (3.5 s squeeze/6.5 s release, 120 blocks) with concurrent cortical activity imaging by functional Near-Infrared Spectroscopy (fNRIS; OMM-3000, Shimadzu Corp., 111 channels). Subjects were grouped as physically active (n = 10) or inactive (n = 12) based on a questionnaire (active-exercise at least four times a week, inactive- exercise less than two times a week). We explored how motor task fatigue affected the vasomotion-induced oscillations in ΔHbO as measured by fNIRS at each hemodynamic frequency band: endothelial component (0.003–0.02 Hz) associated to microvascular activity, neurogenic component (0.02–0.04 Hz) related to intrinsic neuronal activity, and myogenic component (0.04–0.15 Hz) linked to activity of smooth muscles of arterioles. To help understand how these three neurovascular regulatory mechanisms relate to handgrip task performance we quantified several dynamic fNIRS metrics, including directional phase transfer entropy (dPTE), a computationally efficient and data-driven method used as a marker of information flow between cortical regions, and directional connectivity (DC), a means to compute directionality of information flow between two cortical regions. The relationship between static functional connectivity (SFC) and functional connectivity variability (FCV) was also explored to understand their mutual dependence for each frequency band in the context of handgrip performance as fatigued increased. Our findings ultimately showed differences between subject groups across all fNIRS metrics and hemodynamic frequency bands. These findings imply that physical activity modulates neurovascular control mechanisms at the endogenic, neurogenic, and myogenic frequency bands resulting in delayed fatigue onset and enhanced performance. The dynamic cortical network metrics quantified in this work for young, healthy subjects provides baseline measurements to guide future work on older individuals and persons with impaired cardiovascular health.
A Novel Indoline Derivative Ameliorates Diabesity-Induced Chronic Kidney Disease by Reducing Metabolic Abnormalities

Both diabetes and obesity (diabesity) contribute significantly to the development of chronic kidney disease (CKD). In search of new remedies to reverse or arrest the progression of CKD, we examined the therapeutic potential of a novel compound, AN1284, in a mouse model of CKD induced by type 2 diabetes with obesity. Six-week-old BKS Cg-Dock 7m+/+ Leprdb/J mice with type 2 diabetes and obesity were treated with AN1284 (2.5 or 5 mg kg−1 per day) via micro-osmotic pumps implanted subcutaneously for 3 months. Measures included renal, pancreatic, and liver assessment as well as energy utilization. AN1284 improved kidney function in BSK-db/db animals by reducing albumin and creatinine and preventing renal inflammation and morphological changes. The treatment was associated with weight loss, decreased body fat mass, increased utilization of body fat toward energy, preservation of insulin sensitivity and pancreatic β cell mass, and reduction of dyslipidemia, hepatic steatosis, and liver injury. This indoline derivative protected the kidney from the deleterious effects of hyperglycemia by ameliorating the metabolic abnormalities of diabetes. It could have therapeutic potential for preventing CKD in human subjects with diabesity.
Airport PAPI Lamp Image Detection Algorithm Based on Saliency and Configuration Features

A PAPI lamp detection algorithm is proposed based on saliency feature and structural feature to solve the problem of Precision Approach Path Indicator automatic calibration in the civil aviation airport. First, the configuration of aids lighting and relative position feature model of PAPI was studied. Second, obtain a significant Figure according to the calculation method of the salient features of the designed aids light and extracted line features of the shape. Finally, according to the geometrical constraints of the shape features, the stepwise approximation method is used to find the location area of the PAPI lamp, then recognize the color of them. Through the test of the approach and landing process at different airports with different light configurations, the validity and practicability of the algorithm are verified.
Bismuth and Bismuth Telluride Thin Films Deposited by MOCVD upon Tapered Fiber Sections as Q-switches for Fiber Lasers

Lasing regimes of ring erbium fiber lasers passively Q-switched by means of fiber tapers coated with thin crystalline films of either Bi2Te3 or Bi as saturable absorbers (SAs) are investigated. 10–15 μm in diameter and 5–10 mm long fiber tapers are prepared by local chemical etching out of a silica light reflecting cladding of the standard SMF-28 optical fiber. Nanometer thick island films of pure Bi and Bi2Te3 are synthesized on the surface of the fiber tapers by applying the metalorganic chemical vapor deposition (MOCVD). The deposited films are then covered with two different polymers: either polyvinyl acetate (PVA) or polydimethylsiloxane elastomer (PDMSE). The fiber section with polymer-coated SA fiber is installed into the ring laser cavity in tandem with the section of an active fiber. For all the samples a stable generation of microsecond pulses in the Q-switch mode at a wavelength of about 1560 nm is observed. To the best of our knowledge, a passively Q-switched fiber laser with nanometer Bi films as a saturable absorber is implemented for the first time. It is established that even a small increase in the temperature of the surrounding medium leads to a significant increase in the transmission coefficient of polymer-coated tapers with deposited SA films, which may be attributed to a decrease in the refractive index of the polymers. The most significant increase in the transmission coefficient with increasing temperature is observed in tapers coated with PVA, which has a refractive index larger than fused silica.
A Dual Polarized Stacked Antenna for 5G Mobile Devices

A stacked dual polarized antenna for future 5G mobile handsets is presented. Three square patches are realized on a stack of Rogers Duroid RT5880 substrates, and proper air gaps placed on the two lower patches determine the energy coupling. The feeding system consists of a couple of coaxial cable which provides the dual polarization in the same antenna. The results show a bandwidth from 26 to 28 GHz with more than 10 dB return loss for both polarizations and more than 18 dB isolation between the feeds. A directivity of over 9 dB is achieved over the whole operating frequency band for the copolar component of each polarization.
Risk Assessment of Sarcopenia in Patients With Type 2 Diabetes Mellitus Using Data Mining Methods

Purpose: Sarcopenia is a geriatric syndrome, and it is closely related to the prevalence of type 2 diabetes mellitus (T2DM). Until now, the diagnosis of sarcopenia requires Dual Energy X-ray Absorptiometry (DXA) scanning. This study aims to make risk assessment of sarcopenia with support vector machine (SVM) and random forest (RF) when DXA is not available. Methods: Firstly, we recruited 132 patients aged over 65 and diagnosed with T2DM in Changchun, China. Clinical data were collected for predicting sarcopenia. Secondly, we selected 3, 5, and 7 features out of over 40 features of patient's data with backward selection, respectively, to train SVM and RF classification models and regression models. Finally, to evaluate the performance of the models, we performed leave one out and 5-fold cross validation. Results: When training the model with 5 features, the sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were favorable, and it was better than the models trained with 3 features and 7 features. Area under the receiver operating characteristic (ROC) curve (AUC) were over 0.7, and the mean AUC of SVM models was higher than that of RF. Conclusions: Using SVM and RF to make risk assessment of sarcopenia in the elderly is an option in clinical setting. Only 5 features are needed to input into the software to run the algorithm for a primary assessment. It cannot replace DXA to diagnose sarcopenia, but is a good tool to evaluate sarcopenia.
A Deep-Learning-Based Sea Search and Rescue Algorithm by UAV Remote Sensing

With the promotion of economic globalization, the demand for air travel and maritime travel in all countries has increased significantly. However, the accompanying rate of accidents at sea is increasing rapidly year by year. After the accident, the survival rate of people is closely related to the speed of rescue. Artificial eyesight and radar electronic search and rescue (SAR) are limited due to inaccuracy and limited resources. The rapid development of unmanned aerial vehicle (UAV) technology in recent years has made it possible to obtain stable, high-resolution images from airborne cameras. In this paper, a method of SAR using images captured by UAVs is proposed. The detection of victim is divided into two steps. First, in order to improve the system’s real-time, the possible position of a victim may be located from the image through a simple feature to avoid exhaustive search. This step is called hypothesis generation (HG). Then, the pre-trained convolutional neural network (CNN) is used to test the HG region to ensure the accuracy of the system. This step is called hypothesis verification (HV). The result shows that taking advantage of pre-processing + CNN has better comprehensive performance in SAR at sea.