Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning through VR gaming with virtual pink dolphins for children with ASD

ABSTRACT Autism Spectrum Disorder (ASD) is a developmental disorder with different levels of severity. Although the exact causes of ASD is not yet known, nor is there a medical cure for ASD to date, special facilities and schools have been established to help individuals coping better and becoming more independent. With the advancement in Virtual Reality (VR) technology, there has been a greater depth of development of technology-enhanced game-based learning for children with ASD. This paper will describe our effort on virtual pink dolphins to assist children with ASD in their learning, at the same time, to avoid the use of physical pink dolphins which is a species endangered. A study on the use of a low-cost VR enabled pink dolphins game for children with ASD to learn direction following, psychomotor skills and hand-eye coordination will be reported.
Towards a Robust Interactive and Learning Social Robot

Pepper is a humanoid robot, specifically designed for social interaction, that has been deployed in a variety of public environments. A programmable version of Pepper is also available, enabling our focused research on perception and behavior robustness and capabilities of an interactive social robot. We address Pepper perception by integrating state-of-the-art vision and speech recognition systems and experimentally analyzing their effectiveness. As we recognize limitations of the individual perceptual modalities, we introduce a multi-modality approach to increase the robustness of human social interaction with the robot. We combine vision, gesture, speech, and input from an onboard tablet, a remote mobile phone, and external microphones. Our approach includes the proactive seeking of input from a different modality, adding robustness to the failures of the separate components. We also introduce a learning algorithm to improve communication capabilities over time, updating speech recognition through social interactions. Finally, we realize the rich robot body-sensory data and introduce both a nearest-neighbor and a deep learning approach to enable Pepper to classify and speak up a variety of its own body motions. We view the contributions of our work to be relevant both to Pepper specifically and to other general social robots.
Texel Shading

We have developed a texture space shading system built on modern graphics hardware. It begins with a conventional rasterization stage, but records texel accesses as shading work rather than running a shade per pixel. Shading is performed by a separate compute stage, storing the results in a texture. As a baseline, the texels correspond to those required for mipmapped texturing. A final stage collects data from the texture. Storing results in a texture allows for reuse across frames. We also show how adapting shade rate to less than once per pixel further increases performance. We vary shading load to show when these techniques provide a performance win, with up to 4.1x speedup in our experiments at shading times less than 4 ms.
Altitudinal Patterns of Leaf Traits and Leaf Allometry in Bamboo Pleioblastus amarus

Awareness of local-scale variation in leaf traits for a single species and the relationships between these traits and their dependence on altitude might be essential for extrapolating ecophysiological processes from the leaf to the ecosystem level. While altitudinal patterns of leaf traits have been extensively studied in a number of species, little is known about such patterns in bamboos. We analyzed leaf functional traits and leaf allometric relationships of Pleioblastus amarus at three different altitudes (200, 400, and 800 m). With increasing altitude, most functional traits, including leaf length, width, perimeter, area, dry weight, and water content, decreased significantly, while the leaf length:width ratio exhibited a marked increase, resulting in a tendency toward narrow leaves. Specific leaf area first increased, and then decreased, while the change in leaf dry matter content showed the opposite trend. Leaf area was positively correlated with leaf length, leaf width and leaf perimeter, but negatively correlated with the leaf length:width ratio. With increasing altitude, the slopes of these relationships for leaf area first increased, and then decreased. Leaf biomass was positively correlated with leaf length, width, perimeter, and area, with the slopes of the relationships being the same at all altitudes. Thus, the leaves of this bamboo species at middle altitude have the highest specific leaf area and lowest leaf dry matter content. Our findings suggest that this bamboo species has a big potential of growth and morphological plasticity.
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.
An Inference-Based Policy Gradient Method for Learning Options

In the pursuit of increasingly intelligent learning systems, abstraction plays a vital role in enabling sophisticated decisions to be made in complex environments. The options framework provides formalism for such abstraction over sequences of decisions. However most models require that options be given a priori, presumably specified by hand, which is neither efficient, nor scalable. Indeed, it is preferable to learn options directly from interaction with the environment. Despite several efforts, this remains a difficult problem. In this work we develop a novel policy gradient method for the automatic learning of policies with options. This algorithm uses inference methods to simultaneously improve all of the options available to an agent, and thus can be employed in an off-policy manner, without observing option labels. The differentiable inference procedure employed yields options that can be easily interpreted. Empirical results confirm these attributes, and indicate that our algorithm has an improved sample efficiency relative to state-of-the-art in learning options end-to-end.
Denoising Distantly Supervised Open-Domain Question Answering

Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text. Existing DS-QA models usually retrieve related paragraphs from a large-scale corpus and apply reading comprehension technique to extract answers from the most relevant paragraph. They ignore the rich information contained in other paragraphs. Moreover, distant supervision data inevitably accompanies with the wrong labeling problem, and these noisy data will substantially degrade the performance of DS-QA. To address these issues, we propose a novel DS-QA model which employs a paragraph selector to filter out those noisy paragraphs and a paragraph reader to extract the correct answer from those denoised paragraphs. Experimental results on real-world datasets show that our model can capture useful information from noisy data and achieve significant improvements on DS-QA as compared to all baselines.
Preconditioning contractions prevent the delayed onset of myofibrillar dysfunction after damaging eccentric contractions

We examined the mechanisms underlying the positive effect of preconditioning contractions (PCs) on the recovery of muscle force after damaging eccentric contractions (ECCs). The mechanisms underlying the immediate force decrease after damaging ECCs differ from those causing depressed force with a few days’ delay, where reactive oxygen species (ROS) produced by invading immune cells play an important causative role. PCs counteracted the delayed onset force depression and this could be explained by prevention of immune cell invasion, which resulted in decreased myeloperoxidase‐mediated ROS production, hence avoiding cell membrane disruption, calpain activation and degenerative changes in myosin and actin molecules.
Disconnected Recurrent Neural Networks for Text Categorization

Recurrent neural network (RNN) has achieved remarkable performance in text categorization. RNN can model the entire sequence and capture long-term dependencies, but it does not do well in extracting key patterns. In contrast, convolutional neural network (CNN) is good at extracting local and position-invariant features. In this paper, we present a novel model named disconnected recurrent neural network (DRNN), which incorporates position-invariance into RNN. By limiting the distance of information flow in RNN, the hidden state at each time step is restricted to represent words near the current position. The proposed model makes great improvements over RNN and CNN models and achieves the best performance on several benchmark datasets for text categorization.
Analysis and Optimization of Random Caching in Large-Scale Wireless Networks with Multiple Receive Antennas

To improve signal-to-interference ratio (SIR) and make better use of file diversity provided by random caching, we consider the maximal ratio combining (MRC) receiver at each user in a large-scale cache- enabled single-input multi-output (SIMO) network. First, by utilizing tools from stochastic geometry, we derive a tractable expression and closed-form upper and lower bounds for the successful transmission probability in the general SIR threshold regime. We also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. The analytical results reveal that the successful transmission probability increases with the number of receive antennas M at each user in the general SIR threshold regime, the coefficient of the asymptotic outage probability decreases with M and the order gain of the asymptotic outage probability does not depend on M. Then, we consider the successful transmission probability maximization. In the general SIR threshold regime, we consider the maximization of the tight upper bound on the successful transmission probability by optimizing the caching distribution, which is a non-convex problem. We obtain a stationary point, by solving an equivalent difference of convex (DC) programming problem using concave-convex procedure (CCCP). We also obtain a closed-form asymptotically optimal solution in the low SIR threshold regime. The optimization results indicate that files of higher popularity get more storage resources, and the caching distribution becomes more flat when M is larger.
Enteric fever among children: 50 cases in a French tertiary care centre.

Background Enteric fever in France is primarily travel-associated. Characteristics of paediatric cases are scarce and information from field studies in endemic countries might not be generalizable to non-endemic countries. Methods In this retrospective study, we reviewed all cases of typhoid and paratyphoid fever treated in a French paediatric tertiary care centre from 1993 to 2015. Results Fifty cases of enteric fever due to Salmonella enterica serovar Typhi (n = 44) and Paratyphi (n = 6) were identified. Sixty-one percent of the children had travelled to Africa and 34% to the Indian subcontinent. Among travel-associated cases, 85% were visiting friends and relatives (VFR). Ninety-six percent had high fever associated with gastrointestinal symptoms. Anaemia (66%), elevated C-reactive protein (80%), transaminitis (87%) and mild hyponatremia (50%) were the main biological findings. Blood cultures were positive in 90% of cases. Twelve strains (24%) were resistant at least to one antibiotic, and all of them had been isolated since 2003, increasing the resistance rate during this last period to 43% (12/28). Ceftriaxone was administered to 71 patients for a median duration of 6 days (interquartile range (IQR): 4-8). The median time to apyrexia after the onset of treatment was 4 days (IQR: 2-5 days). Complications occurred in nine children with five (10%) presenting neurologic disorders. All 50 patients recovered. Conclusion In France, paediatric enteric fever is mainly a travel-associated disease and occurs in patients returning from a prolonged stay in an endemic area. Children VFR are at high risk and should be a priority target group for pre-travel preventive measures. The increase in antibiotic resistance reflects the situation in endemic countries and is a major concern.
Understanding the Role of Conscious and Automatic Mechanisms in Social Networking Services: A Longitudinal Study

ABSTRACT Given the proliferation of social networking sites (SNSs), it has become important to understand users’ continued attraction and usage of them. Previous works on information systems (IS) and marketing have paid little attention to the role of automatic mechanisms in frequently used IS such as mobile applications and SNSs. We develop an integrated framework to examine the effects of conscious and automatic mechanisms on SNS users’ postadoption behaviors. Considering the unique characteristics of SNSs, we posit user satisfaction, trust in an SNS provider, and prior SNS use as key drivers accelerating the development of SNS habitual use. Longitudinal data collected from 250 Facebook users were tested against the research model using partial least squares. The findings showed that SNS continuance usage is shaped by automatic factor as well as by reasoned conscious factor. In particular, SNS continuance usage is largely explained by SNS habit. User satisfaction, trust in an SNS provider, and prior SNS use were found to be the key enablers of developing SNS habit. Moreover, the analysis results clarify the exact role of relationship management, self-presentation, perceived enjoyment, and trust in an SNS provider in users’ decision-making processes. Implications for research and practice are described.
Semidiscrete Finite Element Analysis of Time Fractional Parabolic Problems: A Unified Approach

In this paper, we consider the numerical approximation of time-fractional parabolic problems involving Caputo derivatives in time of order $\alpha$, $0< \alpha<1$. We derive optimal error estimates for semidiscrete Galerkin FE type approximations for problems with smooth and nonsmooth initial data. Our analysis relies on energy arguments and exploits the properties of the inverse of the associated elliptic operator. We present the analysis in a general setting so that it is easily applicable to various spatial approximations such as conforming and nonconforming FEMs, and FEM on nonconvex domains. The finite element approximation in mixed form is also presented and new error estimates are established for smooth and nonsmooth initial data. Finally, an extension of our analysis to a multi-term time-fractional model is discussed.
Resetting Your Password Is Vulnerable: A Security Study of Common SMS-Based Authentication in IoT Device

Firmware vulnerability is an important target for IoT attacks, but it is challenging, because firmware may be publicly unavailable or encrypted with an unknown key. We present in this paper an attack on Short Message Service (SMS for short) authentication code which aims at gaining the control of IoT devices without firmware analysis. The key idea is based on the observation that IoT device usually has an official application (app for short) used to control itself. Customer needs to register an account before using this app, phone numbers are usually suggested to be the account name, and most of these apps have a common feature, called Reset Your Password, that uses an SMS authentication code sent to customer phone to authenticate the customer when he forgot his password. We found that an attacker can perform brute-force attack on this SMS authentication code automatically by overcoming several challenges, then he can steal the account to gain the control of IoT devices. In our research, we have implemented a prototype tool, called SACIntruder, to enable performing such brute-force attack test on IoT devices automatically. We evaluated it and successfully found 12 zero-day vulnerabilities including smart lock, sharing car, smart watch, smart router, etc. We also discussed how to prevent this attack.
mTOR-Related Brain Dysfunctions in Neuropsychiatric Disorders

The mammalian target of rapamycin (mTOR) is an ubiquitously expressed serine-threonine kinase, which senses and integrates several intracellular and environmental cues to orchestrate major processes such as cell growth and metabolism. Altered mTOR signalling is associated with brain malformation and neurological disorders. Emerging evidence indicates that even subtle defects in the mTOR pathway may produce severe effects, which are evident as neurological and psychiatric disorders. On the other hand, administration of mTOR inhibitors may be beneficial for a variety of neuropsychiatric alterations encompassing neurodegeneration, brain tumors, brain ischemia, epilepsy, autism, mood disorders, drugs of abuse, and schizophrenia. mTOR has been widely implicated in synaptic plasticity and autophagy activation. This review addresses the role of mTOR-dependent autophagy dysfunction in a variety of neuropsychiatric disorders, to focus mainly on psychiatric syndromes including schizophrenia and drug addiction. For instance, amphetamines-induced addiction fairly overlaps with some neuropsychiatric disorders including neurodegeneration and schizophrenia. For this reason, in the present review, a special emphasis is placed on the role of mTOR on methamphetamine-induced brain alterations.
Potential of photoplethysmogram for the detection of calcification and stenosis in lower limb

Early detection of arterial stiffness (AS) and atherosclerosis in lower limb is useful for the detection of cardiovascular and diabetic foot diseases. We used photoplethysmogram for the screening of peripheral arterial disease (PAD), and detection of AS occurred due to calcification. The study included three different groups (15-normal subjects (group-1), 6-subjects with known calcification (group-2) and 13 PAD patients). Compared to group-1, we obtained a significant increase in rise-time (282.00 vs. 305.50, p value = 0.009) and area under rise-time (AUR) (71.075 vs. 76.085, p value = 0.041) in PAD group. Similarly in group-2 significant decreases in AUR (71.075 vs. 60.825, p value = 0.000), area under diastole (136.347 vs. 110.538, p value = 0.001), Area (209.729 vs. 170.202, p value = 0.000) and 'b/a' (0.697 vs. 0.933, p value = 0.020) ratio was obtained compared to group-1 and significant increase in these features were noted in comparison with PAD group. The present finding may aid in the detection of PAD and AS due to calcification and arrange proper treatments plan.
Quantitative and Qualitative Analysis of the Perception of Semi‐Transparent Structures in Direct Volume Rendering

Direct Volume Rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. With DVR semi‐transparency is facilitated to convey the complexity of the data. Unfortunately, semi‐transparency introduces challenges in spatial comprehension of the data, as the ambiguities inherent to semi‐transparent representations affect spatial comprehension. Accordingly, many techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we present our findings obtained from two evaluations investigating the perception of semi‐transparent structures from volume rendered images. We have conducted a user evaluation in which we have compared standard DVR with five techniques previously proposed to enhance the spatial comprehension of DVR images. In this study, we investigated the perceptual performance of these techniques and have compared them against each other in a large‐scale quantitative user study with 300 participants. Each participant completed micro‐tasks designed such that the aggregated feedback gives insight on how well these techniques aid the user to perceive depth and shape of objects. To further clarify the findings, we conducted a qualitative evaluation in which we interviewed three experienced visualization researchers, in order to find out if we can identify the benefits and shortcomings of the individual techniques.
Safety and Lack of Negative Effects of Wearable Augmented-Reality Social Communication Aid for Children and Adults with Autism

There is growing interest in the use of augmented reality (AR) to assist children and adults with autism spectrum disorders (ASD); however, little investigation has been conducted into the safety of devices for AR such as smartglasses. The objective of this report was to assess the safety and negative effects of the Brain Power Autism System (BPAS), a novel AR smartglasses-based social communication aid for people with ASD. A sequential series of 18 children and adults aged 4.4 to 21.5 years (mean 12.2 years) with clinically diagnosed ASD of varying severity used the BPAS. Users and caregivers were interviewed about perceived negative effects and design concerns. Most users were able to wear and use the BPAS (n=16/18, 89%), with most of them reporting no negative effects (n=14/16, 87.5%). Two users reported temporary negative effects: eye strain, dizziness, and nasal discomfort. Caregivers observed no negative effects in users (n=16/16, 100%). Most users (77.8%) and caregivers (88.9%) had no design concerns. This report found no major negative effects in using an AR smartglasses-based social communication aid across a wide age and severity range of people with ASD. Further research is needed to explore longer-term effects of using AR smartglasses in this population.