Miracle-dz/UniFAR-FTUs huggingface dataset:
The Hoaxy Misinformation and Fact-Checking Diffusion Network

Massive amounts of misinformation flood social media like Twitter and Facebook. Digital misinformation includes articles about hoaxes, conspiracy theories, fake news, and other misleading claims. This content has been alleged to disrupt the public debate, leading to questions about its impact on the real world. A number of research questions have been formulated around the ways misinformation spreads, who are its main purveyors, and whether fact-checking efforts can be helpful at mitigating its diffusion. Here we release a large longitudinal dataset from Twitter, consisting of retweeted messages with links to misinformation and fact-checking articles. These data have been collected using Hoaxy (hoaxy.iuni.iu.edu), an open social media analytics platform whose goal is to provide a comprehensive picture of how digital misinformation spreads and competes with fact-checking efforts. The released dataset contains over 20 million retweets, spanning the period from May 2016 to the end of 2017. We provide basic statistics about the data and the associated diffusion networks.
Design of a low control-flow overhead-based software-defined wireless sensor network with link failure

Software-Defined Networking (SDN) provides a flexible way to update and reconfigure the network. Unlike wired-SDNs, Software-Defined Wireless Sensor Networks (SDWSNs) lack in a separate control channel, resulting in high control-flow overhead. In this article, we propose a scheme for sleep scheduling with low control overhead-based flow-entry in the FlowTable to overcome the limitation of control traffic in the SDWSNs. We consider an Energy-consumption-based Connected k-Neighborhood (CKN)-based sleep scheduling approach, which is widely used to allow the network to be k-connected with a minimum number of awake-nodes, therefore prolonging the network lifetime. To limit the control-flow at every epoch in the sleep scheduling, control-flow policies are introduced. In addition, this proposed design in SDWSN is validated with a realistic wireless channel that suffers from random link failure. Extensive simulation results show that the proposed control-flow design outperforms state-of-the-art sleep scheduling in SDWSNs in terms of control-flow size and average response time to update the state of nodes in the data plane.
A context-aware recommendation system based on latent factor model

Recommendation systems are the effective tools that get over the information overload problem, providing users with the most appropriate things by considering their personal preferences. The interaction contextual information is considered to help improve the accuracy of the recommendation results. A few previous studies have tried to put to use contextual information to the recommendation system. In this paper, we propose a new recommendation model called C-LFM, which adds contextual information to Latent Factor Model (LFM) to improve recommendation results. Different from the existing recommendation method, C-LFM regards contextual information as a factor of LFM. Extensive experiments are conducted, and the experimental results present the effectiveness of C-LFM to us.
Susceptibility of 2.4GHz low-power receivers to low frequency EMI

In this paper 2.4GHz ISM band Radio Frequency receivers are analyzed and tested. Electro-Magnetic Interference can couple with antennas, PCB traces and integrated inductors reaching the receiver's input and leading to communication impairment. The main causes of errors are analyzed considering the Low Noise Amplifier responsible for communication errors. Receiver susceptibility was evaluated through measurements, by injecting low-frequency interfering waveforms in the receiver front-end once the wireless communication was established. The susceptibility parameter used was the Packet Error Rate; its definition has been modified to account also for the packets lost. Vulnerability to low-frequency interference was found.
Dynamic switching and short circuit capability of 6.5kV silicon carbide MOSFETs

Electrically robust 6.5kV SiC MOSFETs are investigated for the static and dynamic performance, short circuit capability and safe operation area (SOA). SiC MOSFETs rated at 6.5kV were fabricated with different cell pitches from 12μm to 26μm that are able to withstand short circuit pulses of up to 8μs and have a turn-off SOA at 4400V up to twice the nominal current Inom. The paralleling of four MOSFETs was tested to represent a realistic setup while showing a substantial reduction in the switching loss by more than 80% compared to a silicon IGBT and Diode.
Reliability of pressure pain, vibration detection, and tactile detection threshold measurements in lower extremities in subjects with knee osteoarthritis and healthy controls

Objectives: Quantitative sensory testing (QST) is a method to assess somatosensory function in osteoarthritis (OA), but reliability data on the performance of different QST modalities on different joint structures are missing. The main aims of our study were to assess intertester and intratester reliability of tactile detection thresholds (TDTs), vibration detection thresholds (VDTs), and pressure pain thresholds (PPTs) on different knee joint structures. Method: In total, 32 subjects with knee OA and 32 volunteers with healthy knees participated. TDTs, VDTs, and PPTs were examined on the medial tibial condyle, medial tibiofemoral joint line, and rectus femoris muscle twice on the first visit and once after 1–3 weeks. Results: The intratester and intertester intraclass correlation coefficients (ICCs) of PPT measurements varied from 0.60 to 0.90 on different joint structures, showing good to excellent reliability. Intratester reliability (ICC 0.64–0.76) of VDT measurements was higher than intertester reliability (0.48–0.75). The intertester reliability of TDT measurements was excellent in subjects with knee OA (ICC 0.84–0.86) and good in controls (0.67) on the medial tibial condyle. Intratester reliability of TDT measurements varied greatly. Conclusion: PPT testing is a reliable tool for measuring pain thresholds on different joint structures. The VDT measurement is reliable when taken by the same evaluator. The reliability of TDT measurements depends on the site of the measurement.
Ubidecarenone-Loaded Nanostructured Lipid Carrier (UB-NLC): Percutaneous Penetration and Protective Effects Against Hydrogen Peroxide-Induced Oxidative Stress on HaCaT Cells

The objective of the investigation was to evaluate the percutaneous penetration of a ubidecarenone-loaded nanostructured lipid carrier (UB-NLC) and to illuminate the protective effects of UB-NLC for amelioration of hydrogen peroxide-induced oxidative damage on HaCaT cells. Ubidecarenone (UB) was encapsulated in a nanostructured lipid carrier (NLC), which was manufactured by homogenization. The morphological and dimensional properties of the prepared UB-NLC were studied by freeze-fracture transmission electron microscopy (FF-TEM) and photon correlation spectroscopy (PCS). Percutaneous penetration of UB-NLC was carried out by the Franz diffusion cells method. The change of cellular morphology was identified through a non-invasive time-lapse imaging system. The assessment was achieved via the evaluation of the levels of oxidative stress markers: reactive oxygen species (ROS), superoxide dismutase (SOD), glutathione peroxidase (GSH-PX), and malondialdehyde (MDA). Percutaneous penetration of UB loaded in NLC formulation was enhanced in comparison to free UB. Preincubation of HaCaT cells with UB-NLC attenuated the level of intracellular generation of ROS. Lipid peroxidation was diminished by UB-NLC via inhibition of MDA formation. Pretreatment of cells with UB-NLC reestablished the activity of cellular antioxidant enzymes (SOD and GSH-PX). On the basis of the investigation conducted, results suggest that formulating UB as NLC is advantageous for topical delivery and treatment of oxidative stress-induced human diseases.
New hybrid control for wide input full-bridge LLC resonant DC/DC converter

In order to achieve low switching frequency range and high efficiency for a wide-input LLC resonant converter, a new hybrid control combining pulse-frequency modulation (PFM) and phase-shift pulse-width modulation (PS-PWM) is proposed. In the new control, the LLC converter has two operation modes, i.e PFM and PFM+PS-PWM. The new control scheme and operation principle is analyzed deeply, and then efficiency optimization can be carried out by the proposed design procedure for the resonant tank of LLC converter. Finally, a prototype is developed with input voltage range from 22V to 45V, and rated power of 250W and output voltage of 380V. The experiment shows that the peak efficiency of LLC converter reaches to 96.2%, and verifies the proposed control scheme, design.
Does nutritional labeling increase healthy eating fallacy? An exploration into young Indian’s purchase behavior

Article history: Received August 16, 2013 Received in revised format 12 September 2013 Accepted 1 November 2013 Available online November 1 2013 The paper examines young consumers’ responses towards nutritional labeling, it’s information content and the importance of the functional characteristics of these labels as perceived by young consumers in making informed purchase decisions through personal interviews of 220 respondents using a structured questionnaire. Factor analysis was performed to identify the underlying dimensions among a set of nutritional labeling parameters using principal component analysis. Based on factor analysis, ten factors emerged. Regression analysis and t test indicated that, out the ten factors, only three factors namely ‘Nutritional Belief’, ‘Storage instruction & Information overload’, and ‘Exercise & Nutrition’ were significant. These factors were mainly inclined outside the purview of nutritional labeling purchase influence . It was thus concluded that nutritional labeling had less influence in purchase decisions as far as young consumers were considered. Findings of the study give practical insights on food labeling issues for the food processors and policy makers. © 2013 Growing Science Ltd. All rights reserved.
Fingerprint classification using a deep convolutional neural network

Biometric systems detect authenticity based on users' distinct physiological or behavioral characteristics for purposes of identification and access control. These pattern recognition systems are difficult to bypass when compared to traditional token or password based systems. This paper is proposing a new deep learning architecture for fingerprint recognition. The proposed architecture comprises of a pre-processing stage for extracting texture features from fingerprints, and this stage is performed by using histogram equalization, Gabor enhancement and fingerprint thinning. The pre-processed fingerprints are input into a Deep Convolutional Neural Network classifier. The proposed approach has achieved 98.21% classification accuracy with 0.9 loss. The obtained accuracy is significantly higher than previously reported results on the same dataset, 77%.
A Taxonomy in Robot-Assisted Training: Current Trends, Needs and Challenges

In this paper, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human-Robot Interaction which focuses on how robotic agents and devices can be used to enhance user's performance during a cognitive or physical training task. The proposed taxonomy includes a set of parameters that characterize such systems, in order to highlight the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. Towards this direction, we review related taxonomies in Human Robot Interaction, as well as recent works and applications in Robot-Assisted Training. The motivation of this research is to identify and discuss issues and challenges, focusing on the personalization aspects of a Robot-Assisted Training system.
The price of journeying towards the prize—Commencing nursing students' experiences of working and studying: A qualitative study

AIMS AND OBJECTIVES To explore the experiences of commencing first-year undergraduate nursing students who were studying full time while engaging in 20 or more hours of paid work each week. METHOD Using a qualitative exploratory design, commencing full-time nursing students who were employed in paid work for at least 20 hr per week were interviewed between May-June 2016. Data were thematically analysed using the following approach: data familiarisation, generating initial codes independently, searching and reviewing themes and subthemes, and defining and naming these themes and subthemes. RESULTS Four main themes were identified which illustrated students' experiences of working and studying: (a) "Work is a necessity…not a choice" identified how students relied heavily on the financial income from paid work to support themselves and others during their studies, (b) "Something's got to give" highlighted the sacrifices that needed to be made to avoid negative effects on their studies, (c) "It's a balancing act! " demonstrated how students studied strategically and balanced their workload despite challenges, and lastly (d) "Being supported to work and study" described the overwhelming support from others for students to succeed academically. CONCLUSIONS Despite support, working 20 hr or more per week while studying full time often overwhelmed students' personal resources and negatively impacted on course grades. Inflexible University timetables compounded the challenges experienced by students who struggled to balance work and study commitments. RELEVANCE TO CLINICAL PRACTICE Nursing employers play a pivotal role in enabling students to juggle effectively their work-study commitments, through providing work flexibility. Students may also benefit if the nursing workforce advocates that they be awarded exclusion from selected clinical placement requirements, particularly if the clinical placement focus is closely related to their current nursing employment.
A Homotopy Iterative Hard Thresholding Algorithm With Extreme Learning Machine for Scene Recognition

Vision-based scene recognition aims to find a semantic explanation of a scene image. First, how to learn a sparse feature representation by mapping each cluster of patches into few dimensions is one challenging issue. Sparse coding techniques with <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> norm regularization are widely used to learn sparse features. However, <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> norm regularization may not achieve enough sparsity. Furthermore, the initial value has a great influence on the sparsity of codes during the iterative optimization process. Second, how to learn a classifier in a supervised manner in order to improve the generalization is another issue. This paper therefore proposes a scene recognition method and it mainly includes two processes. One is the homotopy iterative hard thresholding (HIHT) algorithm that encodes the sparse representations of local patches by incorporating an <inline-formula> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula> norm regularization. Furthermore, a homotopy continuity strategy is used to improve the sparsity of feature codes by adaptively tuning the regularization factor from large to small values and using the sparse solution of last iteration as the warm start of the next iteration. The other is the extreme learning machine (ELM)-based classifier. Experimental results in 15-class scene data set have shown that the HIHT algorithm outperforms other unsupervised sparse feature learning algorithms in terms of sparsity and entropy. Meanwhile, the ELM-based scene recognition method outperforms the other state-of-the art methods in terms of recognition accuracy.
A study on the effect of knowledge management on job satisfaction: A case study of texture industry

Article history: Received May 12, 2013 Received in revised format 12 September 2013 Accepted 6 October 2013 Available online October 16 2013 This paper presents an empirical investigation to study the effect of knowledge management on job satisfaction in Iranian texture industry. The proposed study of this paper designs a questionnaire and distributes it among 219 out 230 employees who work for one Iranian texture firm in province of Markazi in Iran. The study uses structural equation modeling to examine different hypotheses based on the implementation of LISREL software package. The results of our investigation indicate that knowledge management positively influences on organizational improvement, organizational learning as well as quality improvement. The results of study also indicate that while there was a negative relationship between organizational improvement and job satisfaction, quality improvement and organizational learning positively influence on job satisfaction. © 2013 Growing Science Ltd. All rights reserved.
A genetic algorithm based feature selection for binary phenotype prediction using structural brain magnetic resonance imaging

Machine learning methods have been increasingly applied to structural brain magnetic resonance imaging (MRI) scans for predicting clinical phenotypes at the individual level. Despite significant methodological developments, reducing the dimensionality of the features extracted from brain MRI data remains a major challenge. In this paper, we propose a genetic algorithm based feature selection approach to binary phenotype prediction using structural brain MRI. We divide the population of individuals into multiple tribes and modify the initialization and evolutionary operations to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe is able to focus on exploring a specific part of the solution space. We also incorporate tribe competition into the evolution process, which allows the tribe that produces better individuals to enlarge its sizes so as to have more individuals to search the sub solution space it explores. We have evaluated our proposed approach against eight wrapper and nine filter feature selection methods on the binary phenotype prediction dataset used in the MICCAI 2014 Machine Learning Challenge. Our results indicate that the proposed approach can identify the optimal feature subset more effectively and is able to produce more accurate binary phenotype prediction.
Comparison of two setups for contactless power measurements for side-channel analysis

When performing side-channel analysis of (passively powered) contactless devices such as RFID tags the side-channel signal is dominated by the carrier field used to power the device. This carrier field should be removed by the measurement setup in order to expose the power consumption of the target device and enable a side-channel analysis. In this paper we describe and compare two methods for measuring the power consumption of contactless devices. The comparison is done experimentally by running applets on a contactless BasicCard and evaluating the signal to noise ratio of the measurements collected with each setup. Our results show that both setups perform similar, indicating that more research is needed to investigate the differences and strengths.
Word Spotting Using Convolutional Siamese Network

We present a method for word spotting using convolutional siamese network. A convolutional siamese network employs two identical convolutional network to rank similarity between two input word images. Once the network is trained, it can then be used to spot not just words with varying writing styles and backgrounds but also to spot out of vocabulary words that are not in the training set. Experiments on the historical Arabic manuscript dataset VML, and on the George Washington dataset shows comparable results with the state of the art.
Hybrid Autoregressive Resonance Estimation and Density Mixture Formant Tracking Model

A novel formant tracker is proposed using the mixture models of $t$ densities (tMMs) for vocal tract resonance frequencies estimated with a hybrid linear prediction (HLP) method. The hybrid integer-cycle pitch-synchronous linear prediction (LP) analysis improves the frequency resolution over voiced segments, leading to closer formant estimates than those provided by other LP methods. In conjunction with HLP, formant trajectories are shown to be more nearly tracked by tMMs than by Gaussian density models. Tests with synthetic voiced and whispered speech as well as with an annotated database confirm better performance than either tMM clustering after formant estimation based on different time-frequency representations or tracking after different LP methods.
Joint Optimization of Spectrum Sensing and Transmit Power in Energy Harvesting-Based Cognitive Radio Networks

Proliferation of mass market applications and all forms of smart devices have driven the explosive growth of wireless sensor data traffic in recent years. In spite of notable advancements in wireless sensor technologies, energy scarcity due to a limited battery capacity still remains a critical impediment to consumer electronics applications. Thus, the energy harvesting (EH) technology, utilizing extra energy collected from radio frequency (RF) signals, is regarded as a promising solution for addressing the battery problem. This paper presents, analyzes, and discusses a joint sensing and power allocation scheme for cognitive radio in conjunction with EH, in which a secondary transmitter can harvest energy from RF signals transmitted by a primary transmitter during spectrum sensing. We formulate a non-convex optimization problem to find the optimal sensing time and power allocation for maximizing energy efficiency, while satisfying the constraints on the amount of harvested energy and interference at primary receivers. Using nonlinear fractional programming, the original problem can be reformulated into a tractable convex one, and an energy efficient resource allocation algorithm is devised while taking into account RF EH. Simulation results are used to verify the optimality of the proposed scheme, where the secondary network can accomplish maximum energy efficiency.
Adaptive Dynamic Programming Based Motion Control of Autonomous Underwater Vehicles

In this paper, Adaptive Dynamic Programming (ADP) technique is utilized to achieve optimal motion control of Autonomous Underwater Vehicle (AUV) System. The paper proposes a model-free based method that takes into consideration the actuator input and obstacle position while tracing an optimal path. The concept of machine learning enables to develop a path-planner which aims to avoid collisions with static obstacles. The ADP approach is realized to approximate the solution of the cost functional for optimization purpose by which the positions of the locally situated obstacles need not be priori-known until they are within a designed approximation safety envelope. The methodology is implemented to achieve the path-planning objective using dynamic programming technique. The Least-squares policy method serves as a recursive algorithm to approximate the value function for the domain, providing an approach for the finite space discrete control system. The concept behind the design of an obstacle-free path finder is to generate an optimal action that minimizes the local cost, defined by a functional, under constrained optimization. The most advantageous value function is described by the Hamilton Jacobi Bellman (HJB) equation, that is impractical to solve using analytical methods. To overcome the complex calculations subject to HJB, a method based on Reinforcement Learning (RL), called ADP is implemented. This paper outlines the concept of machine learning to realize a real time obstacle avoidance system.