Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations between the HEXACO model of personality and gambling involvement, motivations to gamble, and gambling severity in young adult gamblers

Background and aims Substantial research has examined the role of personality in disordered gambling. The predominant model in this work has been the five-factor model (FFM) of personality. In this study, we examined the personality correlates of gambling engagement and gambling severity using a six-dimensional framework known as the HEXACO model of personality, which incorporates FFM characteristics with the addition of honesty–humility. In addition, the potential mediating role of gambling motives in the personality and gambling severity relationship was explored. Methods A sample of undergraduate gamblers (n = 183) and non-gamblers (n = 143) completed self-report measures of the Problem Gambling Severity Index (PGSI) and the Gambling Motives Questionnaire-Financial, as well as self- and observer report forms of the HEXACO-100. Results Logistic regression results revealed that scores on honesty–humility were positively associated with non-gambling over gambling status. Furthermore, it was also found that honesty–humility, agreeableness, and conscientiousness were each uniquely associated with PGSI severity scores. The results of the mediational analyses suggest that each personality factor has different gambling motivational paths leading to PGSI gambling severity. Discussion and conclusions The findings of this study contribute to the literature on behavioral addictions by providing an increased understanding of individual personality factors associated with likelihood of gambling, overall gambling severity, and gambling motives. Ultimately, these findings suggest that the honesty–humility dimension may be a target for the prevention efforts against problematic gambling outcomes.
Analysis Of Strain Induced Pockels Effect In Silicon

The recently discovered Pockels effect in strained silicon has made silicon a promising candidate material for optical modulators and switches. In this paper we investigate the electro-optic effect induced by applied strain gradient in silicon optical waveguides. We also propose a model that simplifies the description of the electro-optic effect in strained silicon providing a tool for design and optimization of optical devices.
Capture Commonsense Knowledge for Sentiment Analysis

This paper presents a new distributed representation based approach to capture commonsense knowledge, which is a very important kind of knowledge for affective computing and sentiment analysis. Currently, we focus on meanings of verbs and related dependency relations. Built upon such dependency relations, a set of vector space models are developed to capture the meaning of verbs and transfer them to related words. To demonstrate the capability of this data-driven approach, we do not incorporate any human knowledge. The results show that we can achieve superior performance on a subset of Winograd Schema challenge dataset, compared with other existing pronoun co-reference and embedding models. Besides that, we also use an example to demonstrate that our model can be potentially used in further sentiment analysis tasks.
GhHAI2, GhAHG3, and GhABI2 Negatively Regulate Osmotic Stress Tolerance via ABA-Dependent Pathway in Cotton (Gossypium hirsutum L.)

The type 2C protein phosphatases (PP2Cs) are well known for their vital roles in plant drought stress responses, but their molecular mechanisms in cotton (Gossypium hirsutum L.) remain largely unknown. Here, we investigated the role of three clade A PP2C genes, namely, GhHAI2, GhAHG3, and GhABI2, in regulating the osmotic stress tolerance in cotton. The transcript levels of GhHAI2, GhAHG3, and GhABI2 were rapidly induced by exogenous abscisic acid (ABA) and polyethylene glycol (PEG) treatment. Silencing of GhHAI2, GhAHG3, and GhABI2 via virus-induced gene silencing (VIGS) improved osmotic tolerance in cotton due to decreased water loss, increase in both relative water content (RWC) and photosynthetic gas exchange, higher antioxidant enzyme activity, and lower malondialdehyde (MDA) content. The root analysis further showed that GhHAI2, GhAHG3, and GhABI2-silenced plants were more responsive to osmotic stress. Yeast two-hybrid (Y2H) and luciferase complementation imaging (LCI) assays further substantiated that GhHAI2, GhAHG3, and GhABI2 interact with the core receptors of ABA signaling, GhPYLs. The expression of several ABA-dependent stress-responsive genes was significantly upregulated in GhHAI2-, GhAHG3-, and GhABI2-silenced plants. Our findings suggest that GhHAI2, GhAHG3, and GhABI2 act as negative regulators in the osmotic stress response in cotton through ABA-mediated signaling.
On the development of IoT systems

A typical IoT system consists of four distinct architectural elements: devices, gateways, cloud and apps. All these elements require special skills in their development. In order to write safe, secure IoT systems, developers must be trained and experienced in four different areas of software development: embedded, cloud, web and mobile. In addition, given the distributed nature of IoT systems, distributed programming skills play a critical role. In this paper we examine the challenges in IoT system development, and summarize our observations and experiences on the necessity and co-presence of different types of software development skills in the design of IoT systems.
Optical flow estimation in ocular endoscopy videos using flownet on simulated endoscopy data

In recent years, endoscopy has been increasingly used for ocular surgeries whenever viewing with a microscope is altered by occlusion or opacity. However, ocular endoscopy suffers from several limitations, including reduced field of view and limited resolution, which may compromise its usability. Hopefully, image processing techniques such as mosaicking and super-resolution could help in alleviating these problems, by artificially enlarging the field and increasing resolution. All these techniques rely on the ability to estimate the optical flow between consecutive frames, which is particularly challenging for those images. This paper investigates the use of the state-of-the-art FlowNet algorithm for motion estimation in ocular endoscopy videos. Because FlowNet is strongly supervised, an artificial dataset of consecutive image pairs with ground truth optical flow is generated using eye fundus photographs from Kaggle's Diabetic Retinopathy Detection dataset. A FlowNet model, initialized on the public Flying Chairs dataset, is fine-tuned on these images. Initial experiments show that, unlike any other optical flow estimation method, this model can successfully capture motion between ocular endoscopy image pairs.
Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Multiple-Input Multiple-Output (MIMO) systems are characterised by increased capacity and improved performance compared to the single-input single-output (SISO) systems. One of the main challenge in the design of MIMO systems is the detection of the transmitted signals due to the interference caused by the multiple simultaneously transmitted symbols from the multiple transmit antennas. Several detection techniques have been proposed in the literature in order to reduce the detection complexity, while maintaining the required quality of service. Among these low-complexity techniques is the Lattice Reduction (LR), which can provide good performance and significantly lower complexity compared to Maximum Likelihood (ML) detector. In this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better Bit Error Ratio (BER) performance and lower complexity than the conventional Lenstra, Lanstra, and Lovasz (LLL) LR-aided detection. Additionally, we propose a hardware implementation for the ELR-aided K-Best detector for a MIMO system equipped with four transmit and four receive antennas. The ELR-aided K-Best detector requires an extra 18% increase in power consumption and an extra 20% in area overhead compared to a regular K-Best detector dispensing with ELR, where this increase in the hardware requirements is needed in order to achieve a 2 dB performance improvement at a bit error rate of 10−5.
Introducing and embedding innovation practices in a UK medical engineering degree course

Exposing medical engineering students to innovation and entrepreneurial practices and training them in concepts and models encountered in industry in a simulated start-up company environment allowed students to explore whether this career route was of interest to them and is expected to enhance the employability of all participants. A module, MedTech BEST (Business and Entrepreneurial Skills Training), was developed to focus on the needs of the medical technology industry sector and piloted at the University of Leeds in 2016/2017. Students acquired the skills and knowledge to be able to pitch a hypothetical medical device product together with its supporting business case, developed over the course of the module, to a panel of experienced judges.
Local Standard Deviation Spectral Clustering

The global scaling parameter σ may cause the typical spectral clustering algorithm NJW failing to discover the true clustering of a data set, especially it contains multiple scales. Although the Self-Turning spectral clustering algorithm can overcome the weakness of NJW by proposing the local scaling parameter σ_i for point i, the local scaling parameter may be affected by outliers. To avoid the deficiencies of the NJW and the Self-Tuning spectral clustering algorithms, the local standard deviation spectral clustering named as SCSD for short is proposed in this paper. The SCSD coined the local standard deviation scaling parameter σ_std_i via the standard deviation of point i with its top p nearest neighbors instead of the local scaling parameter σ_i of point i in Self-Tuning spectral clustering. As a consequence the affinity matrix in SCSD can reflect the original distribution of a data set as far as possible. The power of the proposed SCSD was tested on some benchmark data sets including the challenging synthetic data sets and the real world data sets from UCI machine learning repository, and on the synthetically generated comparative big data with noises. Its performance was compared with that of NJW and Self-Tuning in terms of the popular bench mark metrics including accuracy (Acc), Adjusted Mutual Information(AMI) and Adjusted Rand Index (ARI). The extensive experimental results demonstrate that the proposed SCSD spectral clustering algorithm is superior to NJW and Self-Tuning, and can find the true distribution of the data sets as far as possible and can be applied to detect the pattern of a big data.
ASR for mixed speech using SNMF based separation algorithm

Combined experiment for separation and recognition of the mixed speech signal of two speakers using different features are reported in this work. For separation of speech signals, sparse NMF is used after wavelet decomposition of mixed speech signal. Kaldi toolkit with different features is used for speech recognition having 4-gram language model. For experiment purpose, phonetically balanced 1000 Hindi sentences from AMUAV corpus are used. Simulation is done for different target to mixed speech mixing level. Results show that recognition of speech signals after separation using SNMF based algorithm perform better as compared to the recognition of mixed speech when the target to mixed signal ratio is lower than 20dB. It is also observed that Δ + Δ − Δ feature-based speech recognition model perform better as compare of other models.
Assessment of the Short-Term Effectiveness of Kinesiotaping and Trigger Points Release Used in Functional Disorders of the Masticatory Muscles

Chronic face pain syndrome is a diagnostic and therapeutic problem for many specialists, and this proves the interdisciplinary and complex nature of this ailment. Physiotherapy is of particular importance in the treatment of pain syndrome in the course of temporomandibular joint functional disorders. In patients with long-term dysfunction of masticatory muscles, the palpation examination can localize trigger points, that is, thickening in the form of nodules in the size of rice grains or peas. Latent trigger points located in the muscles can interfere with muscular movement patterns, cause cramps, and reduce muscle strength. Because hidden trigger points can spontaneously activate, they should be found and released to prevent further escalation of the discomfort. Kinesiotaping (KT) is considered as an intervention that can be used to release latent myofascial trigger points. It is a method that involves applying specific tapes to the patient's skin in order to take advantage of the natural self-healing processes of the body. The aim of the study was to evaluate the effect of the kinesiotaping method and trigger points inactivation on the nonpharmacological elimination of pain in patients with temporomandibular disorders. The study was conducted in 60 patients (18 to 35 years old). The subjects were randomly divided into two subgroups of 30 people each. Group KT (15 women and 15 men) were subjected to active kinesiotaping application. Group TrP, composed of 16 women and 14 men, was subjected to physiotherapy with the release of trigger points by the ischemic compression method. The results show that the KT method and TrP inactivation brought significant therapeutic analgesic effects in the course of pain-related functional disorders of the muscles of mastication. The more beneficial outcomes of the therapy were observed after using the KT method, which increased the analgesic effect in dysfunctional patients.
General Algorithm for Fault-Tolerant Virtual Machine Assignments

In the modern data centers based on virtualization, virtual machine assignment (VMA) is the primary research topic to efficiently schedule the cloud resources. It has been proved that the problem of VMA to minimize the maximum access latency is of NP-hard. Most existing works focus on how to assign the virtual machines to the data nodes, but few works are reported on fault-tolerant techniques that is indispensable for the security and reliability of the data center network. This paper presents fault-tolerant algorithms to minimize the maximum data access latency for the problem of VMA with fault virtual machines. The proposed algorithm searches the clique with controllable redundancy in the set of the available virtual machines, and then it selects the machines from the redundant clique and assigns them for the data nodes of the data center network. In the proposed algorithm, the number of redundant virtual machines is controllable. Extensively experimental results show that the maximum access latency is reduced by 10.39%, 5.68%, 9.09%, 5.45% on average, on the popular four network architectures, Tree, VL2, Fat-Tree and BCube, respectively, in comparison to the state-of-the-art of approximation algorithms. For the case of redundancy degree less than 40%, the proposed algorithm can accelerate the assignment for the virtual machines by up to 24% in comparison to the existing algorithm, on the mentioned four popular network architectures.
Phosphatidylcholine Liposomes Down-Modulate CD4 Expression Reducing HIV Entry in Human Type-1 Macrophages

A strategy adopted to combat human immunodeficiency virus type-1 (HIV-1) infection is based on interfering with virus entry into target cells. In this study, we found that phosphatidylcholine (PC) liposomes reduced the expression of the CD4 receptor in human primary type-1 macrophages but not in CD4+ T cells. The down-regulation was specific to CD4, as any effect was not observed in CCR5 membrane expression. Moreover, the reduction of membrane CD4 expression required the Ca2+-independent protein kinase C (PKC), which in turn mediated serine phosphorylation in the intracytoplasmic tail of the CD4 receptor. Serine phosphorylation of CD4 was also associated with its internalization and degradation in acidic compartments. Finally, the observed CD4 downregulation induced by PC liposomes in human primary macrophages reduced the entry of both single-cycle replication and replication competent R5 tropic HIV-1. Altogether, these results show that PC liposomes reduce HIV entry in human macrophages and may impact HIV pathogenesis by lowering the viral reservoir.
Left atrial appendage occlusion with the AMPLATZER Amulet device: one-year follow-up from the prospective global Amulet observational registry.

AIMS Left atrial appendage occlusion (LAAO) is a stroke prevention therapy for patients with non-valvular atrial fibrillation (AF). This study reports one-year outcomes from patients enrolled in the prospective global Amulet registry. METHODS AND RESULTS A total of 1,088 patients were recruited, aged 75±9 years; 65% of patients were male. The CHA2DS2-VASc and HAS-BLED scores were 4.2±1.6 and 3.3±1.1, respectively. Eighty-three percent (83%) of patients had contraindications to anticoagulation (OAC); 72% had a history of major bleeding. An AMPLATZER Amulet LAA occluder was successfully implanted in 99% of cases. Transoesophageal echocardiography one to three months after implant showed no residual flow or flow <3 mm in 98.4%. The observed ischaemic stroke rate was 2.9%/year. Device-related thrombus was noted in 1.7% of patients. There were ten cases between 0 and 90 days and eight cases between 91 and 365 days. Patients discharged without OAC (>80%), in particular those on single aspirin therapy, did not appear to have a higher risk of device-related thrombus. In the first year, major bleeding occurred at an annualised rate of 10.3%. All-cause mortality was 8.4% at one year. CONCLUSIONS In the global prospective Amulet registry of patients at high risk of stroke and bleeding, the annualised ischaemic stroke rate was 2.9%. The LAA was sealed in 98.4% after one to three months and device-related thrombus was observed in 1.7% of cases with only a minority of all patients on anticoagulation treatment.
Burden changes in caregivers of patients with type 2 diabetes: A longitudinal study

AIMS The aim of this study was to explore changes in the burden of caregivers of patients with type 2 diabetes experiencing lower limb amputation after surgery. BACKGROUND Literature suggests the burden overload experienced by the caregivers of new amputees is related to an imbalance between the demands and the resources available to these caregivers. DESIGN The study followed a longitudinal design assessing caregiver burden at 1 (T1), 7 (T2) and 10 (T3) months after the patient's surgery. METHODS We used a convenience sample of caregivers of patients with type 2 diabetes amputated with recent lower limb amputation. Data were collected in several hospital units over 18 months in 2014-2015. Sample size included at T1, T2 and T3 110, 101 and 84. Participants completed the socio-demographic questionnaire, the Burden Assessment Scale and the Self-Assessment Caregiver Questionnaire scale. RESULTS Caregivers who received help reported lower levels of burden from at baseline with no variation over time. Those caregivers with high levels of stress showed an increase in burden over time, although these results were not significant. Also, caregivers who did not receive help showed higher levels of burden and stress over time compared with the initial baseline that decreased over time. CONCLUSION These results highlight the importance of receiving help, in care, especially among caregivers who care for patients who have undergone major amputation.
Construction of a population-based digital atlas of the fetal sheep brain

Introduction The brain may be the most complex, and therefore the least understood, organ in the body. Recent advances in imaging and computing technology are bringing innovative new tools to study the human brain. One such tool is a brain atlas, which contains numerous features allowing for greater insight into the brain's structure and function. Various forms of atlases exist. Some answer questions such as where is a given structure and what is its shape, while others seek to quantify the difference between a normal and diseased brain or measure variability within a given population. Newer atlases have enabled powerful new pathways to integrate disparate data sets creating correlative studies. The imperative step in integrating these distinct data sets is registering the image sets into one common space. Registration templates were built to provide a means for spatial normalization. Several templates exist in the human realm. Nonhuman species are more appropriate for many studies, yet the development of such targets for animal models have lagged behind. The fetal sheep brain is a widely used model in many research efforts, especially in the study of Cerebral Palsy. The existence of a population-based fetal sheep atlas would greatly benefit those studying this animal model. Procedure Five age-matched fetal sheep brains were selected to construct the atlas. The images were acquired at Washington University in Saint Louis, Missouri. These images were processed to extract the tissue form non-tissue. The widely used Automatic Image Registration tool was then used to build an affine atlas. The images were further aligned
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation

We announce a shared task on UCCA parsing in English, German and French, and call for participants to submit their systems. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general. Furthermore, existing applications for semantic evaluation that are based on UCCA will greatly benefit from better automatic methods for UCCA parsing. The competition website is this https URL
MAPP: A Modular Arithmetic Algorithm for Privacy Preserving in IoT

High-speed Internet becomes more widely accessible, the cost of connecting devices is reducing, more devices are being created with Wi-Fi abilities and sensors are built into those devices, technology costs are cost-effective. All of these things are creating a ``storm" for the Internet of Things (IoT) era. With the emergence of the Internet of Things, connect things through internet, there are many devices that are providing data. The more devices, the more data. If these devices want to share data with the aim of achieving abstract knowledge, they need to pass through the network. Some devices provide sensitive information that can be time. We should protect these sensitive data, from unwanted disclosure that is called privacy. In other words, privacy preserving of data is one of the main problems of IoT devices, protecting sensitive data against unauthorized users when we do analysis and even transferring data. To achieve this in this paper, we propose a new method based on modular arithmetic for privacy-preserving when IoT devices produce sensitive time label called MAPP. We applied Number Theory, Modular Arithmetic to achieve that aim. We used CupCarbon 3.0 for simulation and evaluating our proposed method. Performance evaluation shows that our proposed method has good performance from the energy consumption point of view.