Miracle-dz/UniFAR-FTUs huggingface dataset:
Interleukin 10 gene promoter polymorphisms (rs1800896, rs1800871 and rs1800872) and haplotypes are associated with the activity of systemic lupus erythematosus and IL10 levels in an Iranian population

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with unknown aetiology. According to the role of interleukin 10 (IL10) in SLE pathogenesis, the genetic alterations in its promoter region could be associated with elevated IL10 levels and exacerbated disease. Here, we investigated the association of genotype and haplotype frequencies of three IL10 gene promoter polymorphisms with susceptibility to SLE, IL10 plasma levels and disease activity of patients in an Iranian population. A total of 116 SLE patients and 131 healthy subjects were enrolled. The PCR‐RFLP technique was used to detect IL10 promoter genotypes at the positions of −1082 (G/A), −819 (C/T) and −592 (C/A) in association with IL10 plasma levels and SLEDAI scores. The GG genotype of −1082 polymorphism was associated with the increased risk of SLE [OR = 2.65, 95% CI (1.21–5.82), p‐value = 0.046]. The CC genotype in −819 region was associated with SLE susceptibility [OR = 3.38, 95% CI (1.26–9.07), p‐value = 0.034] and C allele was introduced as risk allele [OR = 1.86, 95% CI (1.15–3.01), p‐value = 0.009] in this region. IL10 plasma levels were overexpressed in CC genotype carriers of −592 SNP and decreased in AA genotype carriers of −1082. IL10 was also increased in SLE patients with CGT (−592/−1082/−819) haplotype. The SLEDAI score was higher among CC genotype carriers at the position of −592 and TT genotype carriers at the region of −819. SLEDAI was also elevated among patients with CGC (−592/−1082/−819) and CAC (p = 0.011) haplotypes. The present study suggests that the IL10 –819(C/T), −1082(G/A) and −592(C/A) polymorphisms and the haplotypes are associated with SLE susceptibility, increased disease activity and elevated IL10 levels. While this is the first time to report such an association in an Iranian population, further studies are needed to confirm these findings.
Mining: Students Comments about Teacher Performance Assessment using Machine Learning Algorithms

The exponential expansion of sentiment analysis is mainly due to the interest of companies to obtainment users’ views about products and services. Likewise, sentiment analysis has been widely and successfully used in publicity and selling strategies as well as behaviour patterns and user preferences identification. In the same way, has been applied to educational areas to increase the learning, and e-learning quality through the mining analysis data on teaching performance assessment or student’s comments. This work describes the development and evaluating the process of a Model called “SocialMining“, which is focused on higher education with the purpose of improve teaching techniques of teachers and recommend courses for teacher improvement, through teacher performance evaluation made by students comments. The SocialMining model analyze students’ comments by means representative machine learning algorithms such as: Support Vector Machines and Random Forest. We applied metrics as evaluation measure to measuring the performance of algorithms. Finally, we have implemented this model in Universidad Politecnica de Aguascalientes (Mexico) and the results obtained show that it is feasible to perform sentiment analysis to classify comments of Teacher Performance Assessment using Machine Learning with high accuracy (85%).
A Measurement Method of MIMO Channel Capacity Considering the Base Station Correlation in 3D MIMO-OTA

This paper presents a measurement method for next-generation mobile communication terminals that involve mobile communication multiple-input multiple-output (MIMO) terminals under a small cell propagation environment. A method of the implementation for embedding the base station correlation in the measurement instrument under the small cell propagation environment is described. The measurements of the channel capacity using a $\mathbf{2}\times\mathbf{2}$ MIMO antenna were carried out. The results show that the measured correlation coefficient of the uplink channel agrees well with the theoretical value, confirming the effectiveness of the proposed method.
Array of Eight Circularly Polarized Microstrip Antennas for IEEE 802.11ac MIMO WLAN

An array of eight circularly polarized microstrip antennas is proposed as MIMO antennas for IEEE 802.11ac MIMO WLAN system. The proposed antenna design is implemented at access point side. The eight antennas are split into two groups of four antennas. The antennas belong to the same group are set with the same polarization while the antennas belong to the first group have different polarization from the one of the antennas in the second group. This array of antennas having two different polarization has been investigated. The results show that setting the adjacent elements with different polarizations, such as right-hand and left-hand circular polarizations, allows the suppression of the mutual coupling between antennas. The highest mutual coupling is -26.8 dB. Each antenna element covers 480 MHz bandwidth. We calculate and estimate the MIMO capacity that is supported by the antenna. It can be shown that 1 Gbps capacity can be reached using the proposed antennas configuration.
Oversampled Adaptive Sensing with Random Projections: Analysis and Algorithmic Approaches

Oversampled adaptive sensing (OAS) is a recently proposed Bayesian framework which sequentially adapts the sensing basis. In OAS, estimation quality is, in each step, measured by conditional mean squared errors (MSEs), and the basis for the next sensing step is adapted accordingly. For given average sensing time, OAS reduces the MSE compared to non-adaptive schemes, when the signal is sparse. This paper studies the asymptotic performance of Bayesian OAS, for unitarily invariant random projections. For sparse signals, it is shown that OAS with Bayesian recovery and hard adaptation significantly outperforms the minimum MSE bound for non-adaptive sensing. To address implementational aspects, two computationally tractable algorithms are proposed, and their performances are compared against the state-of-the-art non-adaptive algorithms via numerical simulations. Investigations depict that these low-complexity OAS algorithms, despite their suboptimality, out-perform well-known non-adaptive schemes for sparse recovery, such as LASSO, with rather small oversampling factors. This gain grows, as the compression rate increases.
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting

Long Short-Term Memory (LSTM) is a well-known method used widely on sequence learning and time series prediction. In this paper we deployed stacked LSTM model in an application of weather forecasting. We propose a 2-layer spatio-temporal stacked LSTM model which consists of independent LSTM models per location in the first LSTM layer. Subsequently, the input of the second LSTM layer is formed based on the combination of the hidden states of the first layer LSTM models. The experiments show that by utilizing the spatial information the prediction performance of the stacked LSTM model improves in most of the cases.
Concept Assessment System Integrated with a Knowledge Map Using Deep Learning

Many students face cognitive overload and conceptual and navigational disorientation in massive open online courses. We used the learning logs of videos, the difficulty of exercises, the discrimination of exercises, and the student's ability, combined with deep learning, to determine the student's familiarity and mastery of video content. We then determined a knowledge node score, which we integrated with a knowledge map. The map could be personalized to recommend videos and exercises according to the knowledge nodes that had a low score. Therefore, students could learn where they needed to improve and adjust their learning accordingly. Moreover, teachers and teaching assistants could observe students' knowledge maps to understand their performance and help them accordingly.
Transfer Learning for Industrial Applications of Named Entity Recognition

In this paper, we propose a Transfer Learning technique for Named Entity Recognition that is able to flexibly deal with domain changes. The proposed technique is able to manage both the case when the set of named entities does not change and the case when the set of named entities changes in the target domain. In particular, we focus on the case when the target data contains only the annotation of a target named entity, and the source data is no longer available for the target task. Our solution consists in transferring the parameters from a source model, which are then fine-tuned with the target data. The model architecture is modified when recognizing a new category by adding properly new neurons to the model. Our experiments show that it is possible to effectively transfer learned parameters in both the scenarios, resulting in strong performances over the target categories without degrading the performances on the other named entities.
Nine novel species of Huntiella from southern China with three distinct mating strategies and variable levels of pathogenicity

ABSTRACT The ascomycete genus Huntiella (Microascales) has a cosmopolitan distribution and occurs on a wide range of woody plants. Little is known regarding the identity, diversity, origin, or impact of these fungi in China. Recently, isolates of Huntiella spp. were collected from stumps of freshly felled trees or wounds on plantation-grown Eucalyptus in Guangdong, Guangxi, Fujian, and Hainan provinces of southern China. Additional isolates were obtained from stumps of Acacia confusa near Eucalyptus plantations in Hainan Province. The aim of this study was to identify these Huntiella species and to test their pathogenicity on Eucalyptus seedlings. Morphology and multigene phylogenies of the nuclear rDNA internal transcribed spacer (ITS1-5.8S-ITS2 = ITS) region and partial β-tubulin (BT1) and translation elongation factor 1α (TEF1α) genes revealed nine previously unknown Huntiella species, eight from Eucalyptus and one from A. confusa. The mating types of these species were determined, showing that seven are heterothallic, one is homothallic, and one is unisexual (MAT1-2-1 gene). Pathogenicity tests showed that the nine Huntiella species can produce lesions on Eucalyptus seedlings, larger than wounds caused by controls on these plants. This study provides a basic understanding of the distribution, diversity, and pathogenicity of Huntiella species in southern China.
Validation of an EMG sensor for Internet of Things and Robotics

This paper describes the construction of a proposed EMG sensor which is intended as a future work to apply in the field of technology of Internet of Things (IoT) and exoskeletons. Subsequently, this work presents the validation of the proposed sensor compared with commercial clinical use EMG sensors (Biometrics of Biometrics Ltd. UK and Shimmer 3 of Shimmer Sensing, Ireland) based on Signal to Noise Ratio (SNR) that is being calculated by two different methods. The obtained results from the experimentation show that the sensors Biometrics and Shimmer 3 are around 20dB SNR while, the proposed sensor gets between 11dB and 18dB SNR. Then, it indicates that the proposed sensor is feasible for the application in technologies like IoT (for manipulating home devices through the interpretation of muscle activity) or robotics but is not suitable for applications that involve subjects musculoskeletal monitoring or diagnose.
Varieties of Young Children’s Prosocial Behavior in Zambia: The Role of Cognitive Ability, Wealth, and Inequality Beliefs

By the 3rd year of life, young children engage in a variety of prosocial behaviors, including helping others attain their goals (instrumental helping), responding to others’ emotional needs (comforting), and sharing resources (costly giving). Recent work suggests that these behaviors emerge early, during the first 2 years of life (Svetlova et al., 2010; Thompson and Newton, 2012; Dunfield and Kuhlmeier, 2013). To date, however, work investigating early varieties of prosocial behavior has largely focused on Western samples and has not assessed the impact of poverty and inequality. In this work, we investigate prosocial behavior in 3-year-olds in Zambia, a lower-middle income country with high wealth inequality. Experiments were integrated into a larger public health study along with both objective and subjective (parent) measures of wealth and inequality. Three-hundred-seventy-seven children (Mean age = 36.77 months; SD = 2.26 months) were presented with an instrumental helping task, comforting task, and two steps of a giving task – one with higher cost (children could give away their only resource) and one with lower cost (children had three resources to give). As predicted, rates of prosociality varied hierarchically by the cost of the action: instrumental helping was the most common followed by comforting, lower cost giving, and higher cost giving. All prosocial behaviors were significantly correlated with one another (with the exception of high cost giving), and with general cognitive ability. Objective family wealth did not predict any of the child’s prosocial behaviors. However, subjective beliefs showed that mothers who believed that they had more than others in their village had children who were more likely to engage in instrumental helping, and mothers who believed that village inequality was a problem had children who were more likely to engage in low cost giving. Low cost giving was also more likely for children whose parents reported reading storybooks to them. This suggests that costly giving in the context of pretend play may relate to children’s experience with using stories as representations of real life events. The results suggest both cultural differences and universalities in the development of prosociality and point to environmental factors that influence prosociality.
Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene–environment interaction effect

Most common human diseases are a result from the combined effect of genes, the environmental factors, and their interactions such that including gene–environment (GE) interactions can improve power in gene mapping studies. The standard strategy is to test the SNPs, one‐by‐one, using a regression model that includes both the SNP effect and the GE interaction. However, the SNP‐by‐SNP approach has serious limitations, such as the inability to model epistatic SNP effects, biased estimation, and reduced power. Thus, in this article, we develop a kernel machine regression framework to model the overall genetic effect of a SNP‐set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function andwe model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. Extensive simulations and real data analysis show that our proposed method has correct type I error and can have higher power than score‐based approaches under many situations.
Intelligent Urban Mobility: Pedestrian and Bicycle Seamless Navigation

Mobility is evolving in urban scenarios and multimodality is the key to a more efficient transportation. In this article we propose a multimodal intelligent navigation system for urban indoor and outdoor environments. Our method is based on wearable sensors mounted on different locations on the human body. The algorithm based on a loose INS/GNSS fusion with magnetometers switches intelligently and seamlessly between the transportation modes walking and riding a bicycle. We also propose an algorithm to cover the still unsolved issue regarding coasting and braking periods for bicycle navigation in GNSS denied scenarios. We have performed an extensive measurement campaign of more than 12 km to test the performance of the proposed algorithms and we have used a Precise Point Positioning solution as ground truth to compute the error. We prove that our method is able to successfully estimate the forward speed of the bicycle during coasting or braking periods. Likewise, we prove that our navigation system switches seamlessly between walking and riding a bicycle and is also able to bridge short GNSS outages.
Clustering Analysis for Neurotransmitter Response Profiles of Dynamic PET data

In this paper we investigated clustering as a potential aid in reducing false positive rates when modelling neurotransmitter activation using the lp-ntPET model. The aim was to investigate whether clustering time-activity curves (TACs) before kinetic modelling improves specificity for detecting activation states. We used two popular unsupervised clustering algorithms, k-means and Gaussian Mixture Models (GMM), which were applied prior to voxel-wise kinetic modelling. We generated statistically independent sets of TACs corresponding to [$^{11}$C]raclopride kinetics and we investigated the impact of: (a) the level of noise in the TACs, (b) the activation magnitude and (c) the ratio between the number of TACs with and without activation on the classification accuracy of each clustering approach. The classification performance was assessed by calculating the sensitivity and specificity for each clustering approach and was compared against conventional single-voxel modeling. Results showed that applying clustering before voxelwise kinetic modeling improves classification of TACs between active and non active states, at least for moderate to less noisy TACs. The single voxel modelling results in better specificity at higher noise levels and clustering assures better results at noise levels from 1k counts to 1M counts.
Comparison of Partial Volume Correction Techniques for Lesions Near High Activity Regions

Partial volume effect (PVE) is a major factor affecting PET quantification as it leads to miscalculation of uptake in lesions, thereby resulting in inaccurate diagnosis and staging. Many techniques are being used to correct for PVE but there is still need for better and more accurate results. This study aims at investigating the extent of, and correcting spill-over effect from high activity regions to the surrounding lesions. A hot region (bladder) was simulated using the XCAT2 digital phantom and different activities were allocated to sim- ulate uptake increase during a typical FDG PET examination. Spherical lesions with diameter 10 mm and fixed activity were placed at variable distances from the hot region to investigate spill-over effect as a function of activity and distance from the hot region. Validation was done using a NEMA physical phantom consisting of a hot bottle surrounded by lower activity spheres. Analytical simulations were carried out using Software for Tomographic Image Reconstruction (STIR) package and all reconstructions were done using Ordered Subset Expectation Maximization (OSEM), incorporating all corrections within the reconstruction. The reconstructed images were corrected for PVE using three techniques: incorporating point spread function (PSF) in the reconstruction (PVC_PSF), Geometric Transfer Matrix technique (PVC-GTM) and a recently proposed reconstruction- based background correction technique (PVC-BC). Region of Interest (ROI) analysis was carried out using standardized uptake values (SUV), mean and bias as figures of merit. The simulation study revealed a significant spill-over effect from the hot region to the surroundings (which is nearly independent of scatter effect), thereby causing uptake overestimation in lesions within 15 mm around the hot region. The variation in lesion uptake is 43.3% without correction, 42.4% with PVC_PSF, 7.7% with PVC-GTM (mean value) and 1.5% with PVC-BC. This effect was further aggravated with the use of post-filter, as the hot region activity spreads further to the surroundings, thereby increasing uptake and impairing lesion visibility. This was in agreement with the phantom experiment, as the estimated activity around the hot bottle was as much as 20 times higher than the original activity with No-PVC, 15 times with PVC_PSF, but only 3 times with PVC-BC. This effect reduces however as we move further away from the hot region, and as iteration increases. We can therefore conclude that the proposed background correction technique was efficient in obtaining a stable lesion uptake and reduced bias, thereby improving lesion quantification and visibility. However, there is need to further validate the performance of the proposed technique using real data.
Detecting Integrity Attacks in IoT-based Cyber Physical Systems: a Case Study on Hydra Testbed

The Internet of Things paradigm improves the classical information sharing scheme. However, it has increased the need for granting the security of the connected systems. In the industrial field, the problem becomes more complex due to the need of protecting a large attack surface while granting the availability of the system and the real time response to the presence of threats. In this contribution, we deal with the injection of tampered data into the communication channel to affect the physical system. The proposed approach relies on designing a secure control system by coding the output matrices according to a secret pattern. This pattern is created by using the Fibonacci p-sequences, numeric sequence depending on a key. The proposed method is validated on the Hydra testbed, emulating the industrial control network of a water distribution system.
Reward learning from human preferences and demonstrations in Atari

To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train a deep neural network to model the reward function and use its predicted reward to train an DQN-based deep reinforcement learning agent on 9 Atari games. Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games without using game rewards. Additionally, we investigate the goodness of fit of the reward model, present some reward hacking problems, and study the effects of noise in the human labels.
Empirical Effects of Dynamic Human-Body Blockage in 60 GHz Communications

The millimeter-wave (mmWave) bands and other high frequencies above 6 GHz have emerged as a central component of fifth generation cellular standards to deliver high data rates and ultra-low latency. A key challenge in these bands is blockage from obstacles, including the human body. In addition to the reduced coverage, blockage can result in highly intermittent links where the signal quality varies significantly with motion of obstacles in the environment. The blockages have widespread consequences throughout the protocol stack including beam tracking, link adaptation, cell selection, handover, and congestion control. Accurately modeling these blockage dynamics is therefore critical for the development and evaluation of potential mmWave systems. In this work, we present a novel spatial dynamic channel sounding system based on phased array transmitters and receivers operating at 60 GHz. Importantly, the sounder can measure multiple directions rapidly at high speed to provide detailed spatial dynamic measurements of complex scenarios. The system is demonstrated in an indoor home entertainment type setting with multiple moving blockers. Preliminary results are presented on analyzing this data with a discussion of the open issues toward developing statistical dynamic models.