Miracle-dz/UniFAR-FTUs huggingface dataset:
A Quantitative Survey of Online Impression Formation and Mate Preferences among Saudi Users of Matrimonial Websites

Matrimonial websites are an important element in the online interaction equation. Saudis can create a certain impression of themselves while indicating their potential preferences in a future spouse. Current research contributes to the Walther (1996) model by considering the message and the communication components: sender, channel, receiver, and feedback.Using a quantitative questionnaire survey (N = 302), the results provided an in-depth description of the script Saudi users follow when trying to find a potential spouse through matrimonial websites. Results found Saudi users differ by demographic variables in the self-presentation of their positive and negative attributes on matrimonial websites.The influence of the receivers’ factors differs according to demographic variables and has been partially confirmed; Saudi matrimonial website users have partially different mate preferences from those described by Buss and Schmitt (1993). In addition, male Saudis who use of matrimonial websites differ in their mate preferences according to their demographic variables, as results showed the role of six variables—age, tribe of origin, relationship status, educational level, income level, and religiosity level. Female Saudis who use matrimonial websites differ highly in their mate preferences according to their demographic variables because the results showed the role of all the aforementioned variables except relationship status.
Numerical Solutions for Groundwater Flow in Unsaturated Layered Soil with Extreme Physical Property Contrasts

Abstract In this paper, the numerical solutions for groundwater flow in unsaturated layered soil using the Richards equation are presented. A linearisation process for the nonlinear Richards equation to deal with groundwater flow in unsaturated layered soil is derived. To solve one-dimensional flow in the unsaturated zone of layered soil profiles, flux conservation and the continuity of pressure potential at the interface between two consecutive layers are considered in the numerical model. In addition, a novel method, named the dynamical Jacobian-inverse free method, incorporated with a two-side equilibration algorithm for solving ill-conditioned systems with extreme contrasts in hydraulic conductivity is proposed. The validity of the model is established in numerous test problems by comparing the numerical results with the analytical solutions. The results show that the proposed method can improve convergence and numerical stability for solving groundwater flow in unsaturated layered soil with extreme contrasts in hydraulic conductivity.
Subjective and objective sleep alterations in medication-naïve children and adolescents with autism spectrum disorder: a systematic review and meta-analysis

Abstract Aims This study aimed to summarize the evidence on sleep alterations in medication-naïve children and adolescents with autism spectrum disorder (ASD). Methods We systematically searched PubMed/Medline, Embase and Web of Science databases from inception through March 22, 2021. This study was registered with PROSPERO (CRD42021243881). Any observational study was included that enrolled medication-naïve children and adolescents with ASD and compared objective (actigraphy and polysomnography) or subjective sleep parameters with typically developing (TD) counterparts. We extracted relevant data such as the study design and outcome measures. The methodological quality was assessed through the Newcastle-Ottawa Scale (NOS). A meta-analysis was carried out using the random-effects model by pooling effect sizes as Hedges’ g. To assess publication bias, Egger’s test and p-curve analysis were done. A priori planned meta-regression and subgroup analysis were also performed to identify potential moderators. Results Out of 4277 retrieved references, 16 studies were eligible with 981 ASD patients and 1220 TD individuals. The analysis of objective measures showed that medication-naïve ASD patients had significantly longer sleep latency (Hedges’ g 0.59; 95% confidence interval [95% CI] 0.26 to 0.92), reduced sleep efficiency (Hedges’ g −0.58; 95% CI −0.87 to −0.28), time in bed (Hedges’ g −0.64; 95% CI −1.02 to −0.26) and total sleep time (Hedges’ g −0.64; 95% CI −1.01 to −0.27). The analysis of subjective measures showed that they had more problems in daytime sleepiness (Hedges’ g 0.48; 95% CI 0.26 to 0.71), sleep latency (Hedges’ g 1.15; 95% CI 0.72 to 1.58), initiating and maintaining sleep (Hedges’ g 0.86; 95% CI 0.39 to 1.33) and sleep hyperhidrosis (Hedges’ g 0.48; 95% CI 0.29 to 0.66). Potential publication bias was detected for sleep latency, sleep period time and total sleep time measured by polysomnography. Some sleep alterations were moderated by age, sex and concurrent intellectual disability. The median NOS score was 8 (interquartile range 7.25–8.75). Conclusion We found that medication-naïve children and adolescents with ASD presented significantly more subjective and objective sleep alterations compared to TD and identified possible moderators of these differences. Future research requires an analysis of how these sleep alterations are linked to core symptom severity and comorbid behavioural problems, which would provide an integrated therapeutic intervention for ASD. However, our results should be interpreted in light of the potential publication bias.
Stick-slip squirmers: slip asymmetry can qualitatively change self-swimming characteristics of squirmers

Abstract A slip asymmetry can break the fore–aft symmetry of the local hydrodynamic force distribution on the surface of an otherwise no-slip or uniform-slip particle. Here, we use the Lorentz reciprocal theorem to demonstrate that such asymmetry, even in a fractional amount, can qualitatively alter the swimming characteristics of a self-propelled spherical squirmer, markedly different from those of no-slip or uniform-slip squirmers. Unlike the usual tangential squirming by the thrust-providing B1 mode and the type-determining B2 mode, we discover two unique features for a stick-slip squirmer. First, the squirmer can acquire a swimming velocity U without the B1 mode but simply by a symmetric extensile/contractile squirming from the B2 mode, which is able to reverse the swimming direction of the squirmer. Second, a stresslet $\boldsymbol{\mathsf{S}}$ can also be induced by a unidirectional squirming from the B1 mode, capable of inverting the squirmer's stresslet from extensile type to contractile type or vice versa to change the squirmer from puller to pusher or in a reverse manner. We further show that the two squirming modes can reinforce or compete with each other to enhance or diminish U and $\boldsymbol{\mathsf{S}}$ due to interplays between the asymmetric squirming forces on the stick and the slip faces. A phase diagram is also established to categorize a variety of newly emerging swimming states, such as an enhanced/degraded puller/pusher and a backward puller/pusher, depending on the relative strength of the squirming modes β = B2/B1, the direction of the stick-slip polarity and the degree of the slip disparity. As a result of such cooperative and competitive natures, a stick-slip squirmer can swim more or less efficiently than no-slip and uniform-slip ones. These distinctive features arising from stick-slip disparity can not only be made geometrically tuneable for steering the motion of a squirmer, but also provide new means for making efficient artificial microswimmers using amphiphilic Janus particles.
The Effect of the Software Test Survey for Students in Developing the Arabic Language Skills of Third-Grade Students in Qatar

This paper studies the effect of implementing a new Arabic language learning software Test Survey for Students (TSFS) developed by the researchers and marketed as brain training software for primary students on language skills using Science, Technology, Arts, and Mathematics (STEAM) subjects. The teaching of Arabic language via computer-assisted language learning (CALL) takes place for the first time in Qatari schools specifically for primary students. The researchers included a theoretical analysis of current trends in CALL and Arabic language skills. Reading skills, learning word and sentence skills, and dictation skills were the three Arabic basic skills tested to measure the effectiveness of developing these skills and improve achievement for third-grade students using CALL in STEAM subjects. Mixed models analyzed using the statistical software for social sciences suggest that teaching Arabic language skills with STEAM topics using educational software is effective and has a promising potential to be extended to the whole of middle east and north Africa region.
Absolute-magnetic-field measurement using nanogranular in-gap magnetic sensor with second-harmonic and liquid-nitrogen-temperature operation

To detect the absolute magnetic field, such as the earth’s magnetic field, a linear magnetic response, a zero point, and thermal stability are required. We thus propose an operating method and sensor probe consisting of a nanogranular in-gap magnetic sensor (GIGS), an operational amplifier integrated circuit, and a modulation coil. The sensor probe was operated in second-harmonic mode at a liquid-nitrogen (Liq. N2) temperature. When an AC magnetic field was applied to GIGS, the second-harmonic signal was generated and modulated by the outer magnetic field to be measured. After lock-in detection, the modulated output signal showed good linearity and a zero point. Moreover, higher sensitivity and low noise with low thermal fluctuation was obtained by the cooling at Liq. N2 temperature.
PAID: Perturbed Image Attacks Analysis and Intrusion Detection Mechanism for Autonomous Driving Systems

Modern Autonomous Vehicles (AVs) leverage road context information collected through sensors (e.g., LiDAR, radar, and camera) to support the automated driving experience. Once such information is collected, a neural network model predicts subsequent actions that the AV executes. However, state-of-the-art research findings have shown the possibility that an attacker can compromise the accuracy of the neural network model in predicting tasks. Indeed, mispredicting the subsequent actions can cause harmful consequences to the road user’s safety. In this paper, we analyze the disruptive impact of adversarial attacks on road context-aware Intrusion Detection System (RAIDS) and propose a solution to mitigate such effects. To this end, we implement five state-of-the-art evasion attacks on vehicle camera images the IDS uses to monitor internal vehicular traffic. Our experimental results underline how this type of attack can reduce the attack detection accuracy of such detectors down to 2.83%. To combat such adversarial attacks, we investigate different countermeasure and propose PAID, a robust context-aware IDS that leverage feature squeezing and GPS to detect intrusions. We evaluate PAID’s capability in identifying such attacks, and implementation results confirm that PAID achieves a detection accuracy of up to 93.9%, outperforming RAIDS’s performance.
MesoGen: Designing Procedural On-Surface Stranded Mesostructures

Three-dimensional mesostructures enrich coarse macrosurfaces with complex features, which are 3D geometry with arbitrary topology in essence, but are expected to be self-similar with no tiling artifacts, just like texture-based material models. This is a challenging task, as no existing modeling tool provides the right constraints in the design phase to ensure such properties while maintaining real-time editing capabilities. In this paper, we propose MesoGen, a novel tile-centric authoring approach for the design of procedural mesostructures featuring non-periodic self-similarity while being represented as a compact and GPU-friendly model. We ensure by construction the continuity of the mesostructure: the user designs a set of atomic tiles by drawing 2D cross-sections on the interfaces between tiles, and selecting pairs of cross-sections to be connected as strands, i.e., 3D sweep surfaces. In parallel, a tiling engine continuously fills the shell space of the macrosurface with the so-defined tile set while ensuring that only matching interfaces are in contact. Moreover, the engine suggests to the user the addition of new tiles whenever the problem happens to be over-constrained. As a result, our method allows for the rapid creation of complex, seamless procedural mesostructure and is particularly adapted for wicker-like ones, often impossible to achieve with scattering-based mesostructure synthesis methods.
CG-GNN: A Novel Compiled Graphs-based Feature Extraction Method for Enterprise Social Networks

In this paper, we propose CG-GNN, a novel compiled graphs-based feature extraction method for Enterprise Social Networks (ESNs). For the provider of ESNs, extracting features from a given social graph is essential. However, since the amount of data available for a single enterprise is often limited, it is necessary to utilize data from other enterprises. We hypothesize that each enterprise has its own enterprise-specific features, while there is a general structure underlying in all enterprises. To reflect the hypothesis, our approach introduces “compiled graphs” to capture enterprise-specific features by mapping them through functions dedicated to that enterprise. The graphs are then handled by Graph Neural Networks (GNNs) that are commonly used across all enterprises to extract general structural information. Therefore, the obtained representations by CG-GNN are balanced in terms of enterprise-specific and enterprise-generic characteristics. Through experiments with private and publicly available datasets, we show that CG-GNN outperforms baselines by a large margin. In a practical scenario, we compute the ideal input of the proposed method for the purpose of ESNs revitalization. This experiment also demonstrates its feasibility and we believe the results are useful for many ESN providers.
DE-NeRF: DEcoupled Neural Radiance Fields for View-Consistent Appearance Editing and High-Frequency Environmental Relighting

Neural Radiance Fields (NeRF) have shown promising results in novel view synthesis. While achieving state-of-the-art rendering results, NeRF usually encodes all properties related to geometry and appearance of the scene together into several MLP (Multi-Layer Perceptron) networks, which hinders downstream manipulation of geometry, appearance and illumination. Recently researchers made attempts to edit geometry, appearance and lighting for NeRF. However, they fail to render view-consistent results after editing the appearance of the input scene. Moreover, high-frequency environmental relighting is also beyond their capability as lighting is modeled as Spherical Gaussian (SG) and Spherical Harmonic (SH) functions or a low-resolution environment map. To solve the above problems, we propose DE-NeRF to decouple view-independent appearance and view-dependent appearance in the scene with a hybrid lighting representation. Specifically, we first train a signed distance function to reconstruct an explicit mesh for the input scene. Then a decoupled NeRF learns to attach view-independent appearance to the reconstructed mesh by defining learnable disentangled features representing geometry and view-independent appearance on its vertices. For lighting, we approximate it with an explicit learnable environment map and an implicit lighting network to support both low-frequency and high-frequency relighting. By modifying the view-independent appearance, rendered results are consistent across different viewpoints. Our method also supports high-frequency environmental relighting by replacing the explicit environment map with a novel one and fitting the implicit lighting network to the novel environment map. Experiments show that our method achieves better editing and relighting performance both quantitatively and qualitatively compared to previous methods.
The Analysis of Factors Influencing Digital Payment in Cambodia

This research aimed to investigate the factors influencing digital payment systems in Cambodia. To provide a comprehensive explanation of the factors that significantly affect digital payment usage, the analyzing model of the Structural Equation Model (SEM) was employed. We established twenty-five items to predict five unobserved constructs: Behavioral Intention, Ease of Use, Social Influences, Risk, and Trust. The Confirmative Factor Analysis (CFA) indicated that among the constructs, the Risk factor was the only factor eliminated. Given that its factor loading is less than 0.5. Besides, we employed the estimated method of the Maximum Likelihood Estimation (MLE) regression model in this study. The model estimated result instituted that among the four observed constructs, the Ease of Use and the Social Influence factors were found to have a significant favourable influence on the people's Behavioral Intention in using Mobile Banking Applications and Internet Banking in Cambodia. The null hypothesis of the Ease of Use and the Social Influence constructs were rejected at 1 percent and 10 percent levels, respectively. In contrast, the other two constructs, the Risk and Trust factors, were insignificant with B.I. Giving both their null hypothesis construct elements had no significant impact on the BI and were rejected at 5 percent level.
Covid 19’s Impact on Stocks'Relative Risks

To understand the impact of the pandemic on different stocks ‘relative risks, we analyze the stock market by comparing pre-covid and post-covid statistics. By using the Capital Asset Pricing Model (CAPM) [1] to fit our data, our result shows interesting trends on both individual and industry levels. There are three significant implications of the result of our research: 1)The R-Square of most stocks’ increased after the outbreak of Covid; 2)The consumer staples, real estate, and utilities sectors experienced a much more significant increase in R-square than other sectors; 3)Companies with stocks with a more significant decrease in their CAPM's β factor are less negatively influenced by COVID-19, and companies with stocks with a more significant increase in their CAPM's β factor are in the utilities and real estate sectors.
Real-time Collision using AI

Traditional narrow collision pipelines for rigid body dynamics typically consist of calculation of collisions between primitives with a dedicated deepest point function for each primitive pair and a much slower general collision calculation between a pair of convex shapes. The mixture of different collision functions makes these methods not CPU-cache friendly and unsuitable for modern GPU hardware. For many reinforcement learning applications, data transfers become the bottle-neck of the whole pipeline. To get a stable behavior of rigid bodies even on a flat ground without jittering, it is necessary to use multiple contact points for each pair of objects. This is achieved by either keeping track of old collision points, or by adding noise to the collision pair. Using the first approach we get a full set of collision points only after multiple frames, using the second, the computational cost largely increases by a factor equal to the number of samples. In this project, we propose to tackle these issues by introducing: a) the quadric decomposition - a data representation for both primitives and general shapes, describing each object as a union of the intersection of planar and quadric inequalities, b) calculation of the deepest point as a maximization of depth over a set of quadric inequalities, c) prediction of the desired number of contact points as a maximization of the surface of the intersection polygon, d) Recurrent neural network (RNN) based solver to speed up the optimization process.
Anomaly Detection Framework for Securing Next Generation Networks of Platoons of Autonomous Vehicles in a Vehicle-to-Everything System

We consider a security setting involving a platoon of autonomous vehicles (AVs) that commute from one place to another. Such vehicle platooning is utilized to optimize the usage and safety of highways. We propose a dynamic framework for a network of platoons that captures both the communication between different platoons along with the communication between different AVs within the single platoon. We propose an authenticity score scheme for monitoring the behavior of the platoons. We also propose a two-phase anomaly detection within a single platoon to elect and maintain a benign platoon leader. We then propose a long-short term memory (LSTM)-based RSU level anomaly detection scheme to safeguard the whole network of platoons. Finally, we adapt group-based signatures and channel switching schemes for ensuring that the communication channels between AVs and platoons stay secure against man-in-the-middle and denial of service attacks. We perform extensive numerical simulations to evaluate the different components in our framework.
Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction

Ergonomic efficiency is essential to the mass and prolonged adoption of VR/AR experiences. While VR/AR head-mounted displays unlock users’ natural wide-range head movements during viewing, their neck muscle comfort is inevitably compromised by the added hardware weight. Unfortunately, little quantitative knowledge for understanding and addressing such an issue is available so far. Leveraging electromyography devices, we measure, model, and predict VR users’ neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment. Specifically, by learning from collected physiological data, we develop a bio-physically inspired computational model to predict neck MCL under diverse head kinematic states. Beyond quantifying the cumulative MCL of completed head movements, our model can also predict potential MCL requirements with target head poses only. A series of objective evaluations and user studies demonstrate its prediction accuracy and generality, as well as its ability in reducing users’ neck discomfort by optimizing the layout of visual targets. We hope this research will motivate new ergonomic-centered designs for VR/AR and interactive graphics applications. Source code is released at: https://github.com/NYU-ICL/xr-ergonomics-neck-comfort.
Perspective-Correct VR Passthrough Without Reprojection

Virtual reality (VR) passthrough uses external cameras on the front of a headset to allow the user to see their environment. However, passthrough cameras cannot physically be co-located with the user’s eyes, so the passthrough images have a different perspective than what the user would see without the headset. Although the images can be computationally reprojected into the desired view, errors in depth estimation, view-dependent effects, and missing information at occlusion boundaries can lead to undesirable artifacts. We propose a novel computational camera that directly samples the rays that would have gone into the user’s eye, several centimeters behind the sensor. Our design contains an array of lenses with an aperture behind each lens, and the apertures are strategically placed to allow through only the desired rays. The resulting thin, flat architecture has suitable form factor for VR, and the image reconstruction is computationally lightweight, enabling low-latency passthrough. We demonstrate our approach experimentally in a fully functional binocular passthrough prototype with practical calibration and real-time image reconstruction. Finally, we experimentally validate that our camera captures the correct perspective for VR passthrough, even in the presence of transparent objects, specular highlights, and complex occluding structures.