Miracle-dz/UniFAR-FTUs huggingface dataset:
How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques

Online social platforms have been the battlefield of users with different emotions and attitudes toward each other in recent years. While sexism has been considered as a category of hateful speech in the literature, there is no comprehensive definition and category of sexism attracting natural language processing techniques. Categorizing sexism as either benevolent or hostile sexism is so broad that it easily ignores the other categories of sexism on social media. Sharifirad S and Matwin S 2018 proposed a well-defined category of sexism including indirect harassment, information threat, sexual harassment and physical harassment, inspired from social science for the purpose of natural language processing techniques. In this article, we take advantage of a newly released dataset in SemEval-2018 task1: Affect in tweets, to show the type of emotion and intensity of emotion in each category. We train, test and evaluate different classification methods on the SemEval- 2018 dataset and choose the classifier with highest accuracy for testing on each category of sexist tweets to know the mental state and the affectual state of the user who tweets in each category. It is a nice avenue to explore because not all the tweets are directly sexist and they carry different emotions from the users. This is the first work experimenting on affect detection this in depth on sexist tweets. Based on our best knowledge they are all new contributions to the field; we are the first to demonstrate the power of such in-depth sentiment analysis on the sexist tweets.
A Semi-Supervised Approach to Message Stance Classification

Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely on determining the constituent messages’ stance towards the rumour, a feature known as the “wisdom of the crowd. ” Although several supervised machine-learning approaches have been proposed to tackle the message stance classification problem, these have numerous shortcomings. In this paper, we argue that semi-supervised learning is more effective than supervised models and use two graph-based methods to demonstrate it. This is not only in terms of classification accuracy, but equally important, in terms of speed and scalability. We use the Label Propagation and Label Spreading algorithms and run experiments on a dataset of 72 rumours and hundreds of thousands messages collected from Twitter. We compare our results on two available datasets to the state-of-the-art to demonstrate our algorithms’ performance regarding accuracy, speed, and scalability for real-time applications.
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.
Improving Reproducibility in Synthetic Biology

Synthetic biology holds great promise to deliver transformative technologies to the world in the coming years. However, several challenges still remain to be addressed before it can deliver on its promises. One of the most important issues to address is the lack of reproducibility within research of the life sciences. This problem is beginning to be recognised by the community and solutions are being developed to tackle the problem. The recent emergence of automated facilities that are open for use by researchers (such as biofoundries and cloud labs) may be one of the ways that synthetic biologists can improve the quality and reproducibility of their work. In this perspective article, we outline these and some of the other technologies that are currently being developed which we believe may help to transform how synthetic biologists approach their research activities.
Stimulating STDP to Exploit Locality for Lifelong Learning without Catastrophic Forgetting

Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data. For a deployed system that must learn online from an uncontrolled and unknown environment, the ordering of input samples often fails to meet this criterion, making lifelong learning a difficult challenge. We exploit the locality of the unsupervised Spike Timing Dependent Plasticity (STDP) learning rule to target subsets of a segmented Spiking Neural Network (SNN) to adapt to novel information while protecting the information in the remainder of the SNN from catastrophic forgetting. In our system, novel information triggers stimulated firing, inspired by biological dopamine signals, to boost STDP in the synapses of neurons associated with outlier information. This targeting controls the forgetting process in a way that reduces accuracy degradation while learning new information. Our preliminary results on the MNIST dataset validate the capability of such a system to learn successfully over time from an unknown, changing environment, achieving up to 93.88% accuracy for a completely disjoint dataset.
Editorial: The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits

Since the establishment of the mixed linear model (MLM) method for genome-wide association studies (GWAS) by Zhang et al. (2005) and Yu et al. (2006), a series of new MLM-based methods have been proposed (Feng et al., 2016). These methods have been widely used in genetic dissection of complex and omics-related traits (Figure 1), especially in conjunction with the development of advanced genomic sequencing technologies. However, most existing methods are based on single marker association in genome-wide scans with population structure and polygenic background controls. To control false positive rate, Bonferroni correction for multiple tests is frequently adopted. This stringent correction results in the exclusion of important loci, especially for large experimental error inherent in field experiments of crop genetics. To address this issue, multilocus GWAS methodologies have been recommended, i.e., mrMLM (Wang et al., 2016), ISIS EMBLASSO (Tamba et al., 2017), pLARmEB (Zhang et al., 2017), FASTmrEMMA (Wen et al., 2018a), pKWmEB (Ren et al., 2018), and FASTmrMLM (Zhang and Tamba, 2018). Here we summarize their advantages and potential limitations for using these methods (Table 1).
Illumination Invariant Foreground Object Segmentation using ForeGANs

The foreground segmentation algorithms suffer performance degradation in the presence of various challenges such as dynamic backgrounds, and various illumination conditions. To handle these challenges, we present a foreground segmentation method, based on generative adversarial network (GAN). We aim to segment foreground objects in the presence of two aforementioned major challenges in background scenes in real environments. To address this problem, our presented GAN model is trained on background image samples with dynamic changes, after that for testing the GAN model has to generate the same background sample as test sample with similar conditions via back-propagation technique. The generated background sample is then subtracted from the given test sample to segment foreground objects. The comparison of our proposed method with five state-of-the-art methods highlights the strength of our algorithm for foreground segmentation in the presence of challenging dynamic background scenario.
Brain Modulation by Electric Currents in Fibromyalgia: A Structured Review on Non-invasive Approach With Transcranial Electrical Stimulation

Fibromyalgia syndrome (FMS) is a complex disorder where widespread musculoskeletal pain is associated with many heterogenous symptoms ranging from affective disturbances to cognitive dysfunction and central fatigue. FMS is currently underdiagnosed and often very poorly responsive to pharmacological treatment. Pathophysiology of the disease remains still obscure even if in the last years fine structural and functional cerebral abnormalities have been identified, principally by neurophysiological and imaging studies delineating disfunctions in pain perception, processing and control systems. On such basis, recently, neurostimulation of brain areas involved in mechanism of pain processing and control (primary motor cortex: M1 and dorsolateral prefrontal cortex: DLPFC) has been explored by means of different approaches and particularly through non-invasive brain stimulation techniques (transcranial magnetic and electric stimulation: TMS and tES). Here we summarize studies on tES application in FMS. The great majority of reports, based on direct currents (transcranial direct currents stimulation: tDCS) and targeting M1, showed efficacy on pain measures and less on cognitive and affective symptoms, even if several aspects as maintenance of therapeutical effects and optimal stimulation parameters remain to be established. Differently, stimulation of DLPFC, explored in a few studies, was ineffective on pain and showed limited effects on cognitive and affective symptoms. Very recently new tES techniques as high-density tDCS (HD-tDCS), transcranial random noise stimulation (tRNS) and tDCS devices for home-based treatment have been explored in FMS with interesting even if very preliminary results opening interesting perspectives for more effective, well tolerated, cheap and easy therapeutic approaches.
A Unified Dissertation on Bearing Rigidity Theory

This work focuses on bearing rigidity theory, namely, the branch of knowledge investigating the structural properties necessary for multielement systems to preserve the interunit bearings under deformations. The contributions of this work are two-fold. The first one consists in the development of a general framework for the statement of the principal definitions and properties of bearing rigidity. We show that this approach encompasses results existing in the literature, and provides a systematic approach for studying bearing rigidity on any differential manifold in <inline-formula><tex-math notation="LaTeX">$SE(3)^n$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of agents. The second contribution is the derivation of a general form of the rigidity matrix, a central construct in the study of rigidity theory. We provide a necessary and sufficient condition for the infinitesimal rigidity of a bearing framework as a property of the rank of the rigidity matrix. Finally, we present two examples of multiagent systems not encountered in the literature and we study their rigidity properties using the developed methods.
wavEMS: Improving Signal Variation Freedom of Electrical Muscle Stimulation

There has been a long history in electrical muscle stimulation (EMS), which has been used for medical and interaction purposes. Human-computer interaction (HCI) researchers are now working on various applications, including virtual reality (VR), notification, and learning. For the electric signals applied to the human body, various types of waveforms have been considered and tested. In typical applications, pulses with short duration are applied, however, many perspectives are required to be considered. In addition to the duration and polarity of the pulse/waves, the wave shapes can also be an essential factor to consider. A problem of conventional EMS toolkits and systems are that they have a limitation to the variety of signals that it can produce. For example, some may be limited to monophonic pulses. Furthermore, they are usually limited to rectangular pulses and a limited range of frequencies, and other waveforms cannot be produced. These kinds of limitations make us challenging to consider variations of EMS signals in HCI research and applications. The purpose of “wavEMS” is to encourage testing of a variety of waveforms for EMS, which can be manipulated through audio output. We believe that this can help improve HCI applications, and to open up new application areas.
Oxidative Stress Related to Iron Metabolism in Relapsing Remitting Multiple Sclerosis Patients With Low Disability

Oxidative status may play a role in chronic inflammation and neurodegeneration which are considered critical etiopathogenetic factors in Multiple Sclerosis (MS), both in the early phase of the disease and in the progressive one. The aim of this study is to explore oxidative status related to iron metabolism in peripheral blood of stable Relapsing-Remitting MS with low disability. We studied 60 Relapsing-Remitting MS patients (age 37.2 ± 9.06, EDSS median 1.0), and 40 healthy controls (age 40.3 ± 10.86). We measured total hydroperoxides (dROMs test) and Total Antioxidant Status (TAS), along with the iron metabolism biomarkers: Iron (Fe), ferritin (Ferr), transferrin (Tf), transferrin saturation (Tfsat), and ceruloplasmin (Cp) panel biomarkers [concentration (iCp) and enzymatic activity (eCp), copper (Cu), ceruloplasmin specific activity (eCp:iCp), copper to ceruloplasmin ratio (Cu:Cp), non-ceruloplasmin copper (nCp-Cu)]. We computed also the Cp:Tf ratio as an index of oxidative stress related to iron metabolism. We found lower TAS levels in MS patients than in healthy controls (CTRL) and normal reference level and higher dROMs and Cp:Tf ratio in MS than in healthy controls. Cp and Cu were higher in MS while biomarkers of iron metabolism were not different between patients and controls. Both in controls and MS, dROMs correlated with iCp (CTRL r = 0.821, p < 0.001; MS r = 0.775 p < 0.001) and eCp (CTRL r = 0.734, p < 0.001; MS r = 0.820 p < 0.001). Moreover, only in MS group iCp correlated negatively with Tfsat (r = -0.257, p = 0.047). Dividing MS patients in “untreated” group and “treated” group, we found a significant difference in Fe values [F(2, 97) = 10.136, p < 0.001]; in particular “MS untreated” showed higher mean values (mean = 114.5, SD = 39.37 μg/dL) than CTRL (mean 78.6, SD = 27.55 μg/dL p = 0.001) and “MS treated” (mean = 72.4, SD = 38.08 μg/dL; p < 0.001). Moreover, “MS untreated” showed significantly higher values of Cp:Tf (mean = 10.19, SD = 1.77∗10-2; p = 0.015), than CTRL (mean = 9.03, SD = 1.46 ∗10-2). These results suggest that chronic oxidative stress is relevant also in the remitting phase of the disease in patients with low disability and short disease duration. Therefore, treatment with antioxidants may be beneficial also in the early stage of the disease to preserve neuronal reserve.
Advances in Quantitative Imaging of Genetic and Acquired Myopathies: Clinical Applications and Perspectives

In the last years, magnetic resonance imaging (MRI) has become fundamental for the diagnosis and monitoring of myopathies given its ability to show the severity and distribution of pathology, to identify specific patterns of damage distribution and to properly interpret a number of genetic variants. The advances in MR techniques and post-processing software solutions have greatly expanded the potential to assess pathological changes in muscle diseases, and more specifically of myopathies; a number of features can be studied and quantified, ranging from composition, architecture, mechanical properties, perfusion, and function, leading to what is known as quantitative MRI (qMRI). Such techniques can effectively provide a variety of information beyond what can be seen and assessed by conventional MR imaging; their development and application in clinical practice can play an important role in the diagnostic process and in assessing disease course and treatment response. In this review, we briefly discuss the current role of muscle MRI in diagnosing muscle diseases and describe in detail the potential and perspectives of the application of advanced qMRI techniques in this field.
Different Features of Bilingualism in Relation to Executive Functioning

The notion that the long-term practice of managing two languages is beneficial for the executive control system is an ongoing debate. Criticism have been raised that studies demonstrating a bilingual advantage often suffer from small sample sizes, and do not control for fluid intelligence as a possible confound. Taking those suggested factors into account, focusing on older bilingual age groups and investigating the potential effects of linguistic distances, this study aimed to improve the interpretations of the bilinguals’ advantages. Measures of inhibition (Flanker, Stroop, Simon task) and switching (Number-letter, Color-Shape, Local-global task) were collected in participants in the ages 50–75 years (n = 193). Despite a large study sample, results did not support any beneficial effects related to improve processing costs in executive functioning. Sub-analyses of the two different language groups (Swedish–Finnish / Swedish–English) intended to investigate the effect of linguistic distances did not change this outcome. Future studies exploring the potential long-term term effects of bilingualism would benefit from identifying tests of cognitive control with greater ecological validity and include other measures of cognitive functioning. Language learning interventions may also be a promising tool for future research.
Action Observation With Dual Task for Improving Cognitive Abilities in Parkinson’s Disease: A Pilot Study

Action observation therapy (AOT) has been recently proposed as a new rehabilitation approach for treatment of motor deficits in Parkinson’s disease. To date, this approach has never been used to deal with cognitive deficits (e.g., deficits in working memory, attention), which are impairments that are increasingly recognized in Parkinsonian patients. Typically, patients affected by these dysfunctions have difficulty filtering out irrelevant information and tend to lose track of the task goal. In this paper, we propose that AOT may also be used to improve cognitive abilities of Parkinsonian patients if it is used within a dual task framework. We articulate our hypothesis by pivoting on recent findings and on preliminary results that were obtained through a pilot study that was designed to test the efficacy of a long-term rehabilitation program that, for the first time, uses AOT within a dual task framework for treating cognitive deficits in patients with Parkinson’s disease. Ten Parkinson’s disease patients underwent a 45-min treatment that consisted in watching a video of an actor performing a daily-life activity and then executing it while performing distractive tasks (AOT with dual task). The treatment was repeated three times per week for a total of 4 weeks. Patients’ cognitive/motor features were evaluated through standard tests four times: 1 month before treatment, the first and the last day of treatment and 1 month after treatment. The results show that this approach may provide relevant improvements in cognitive aspects related to working memory (verbal and visuospatial memory) and attention. We discuss these results by pivoting on literature on action observation and recent literature demonstrating that the dual task method can be used to stimulate cognition and concentration. In particular, we propose that using AOT together with a dual task may train the brain systems supporting executive functions through two mechanisms: (i) stimulation of goal setting within the mirror neuron system through action observation and (ii) working memory and persistent goal maintenance through dual task stimuli.
Contextual Considerations for Teaching Online at the Postsecondary Level.

Designing an online class guided by the principles of learner-centered instruction at the postsecondary level is a daunting challenge for new professors facing the demands of a tenure track position. This paper presents the findings of a pilot project on two junior faculty members' experiences with online course development and teaching in a university setting. The purpose of the study was to identify and describe those experiences in ways that would be useful to academic administrators, and in ways that would provide a robust basis for conducting a larger descriptive study. The professors' responses to the following research questions are provided: How do the professors describe the experience of planning and teaching an online course? How do the professors perceive the demands of teaching a class online compare with the demands of teaching a similar class face-to-face? How do the professors understand the context of online course development and teaching? Findings are discussed in terms of interaction, importance of students' expectations, and pressures in regard to tenure documentation. The study found that an online environment presents challenges unique to the technology, and the recommendation is made that university administrators must recognize such challenges in order to encourage pedagogically sound online instruction. (Author/AEF) Reproductions supplied by EDRS are the best that can be made from the original document. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
Hidden Symmetries and Geodesics of Kerr spacetime in Kaluza-Klein Theory

The Kerr spacetime in the Kaluza-Klein theory describes a rotating black hole in four dimensions from the Kaluza-Klein point of view and involves the signature of an extra dimension that shows up through the appearance of the electric and dilaton charges. In this paper, we study the separability properties of the Hamilton-Jacobi equation for geodesics and the associated hidden symmetries in the spacetime of the Kerr-Kaluza-Klein black hole. We show that the complete separation of variables occurs only for massless geodesics, implying the existence of hidden symmetries generated by a second rank conformal Killing tensor. Employing a simple procedure built up on an “effective” metric, which is conformally related to the original spacetime metric and admits a complete separability structure, we construct the explicit expression for the conformal Killing tensor. Next, we study the properties of the geodesic motion in the equatorial plane, focusing on the cases of static and rotating Kaluza-Klein black holes separately. In both cases, we obtain the defining equations for the boundaries of the regions of existence, boundedness and stability of the circular orbits as well as the analytical formulas for the orbital frequency, the radial and vertical epicyclic frequencies of the geodesic motion. Performing a detailed numerical analysis of these equations and frequencies, we show that the physical effect of the extra dimension amounts to the significant enlarging of the regions of existence, boundedness and stability towards the event horizon, regardless of the classes of orbits.