Reviewerly/exHarmony huggingface dataset:
ARabic-Kafa: Design and development of educational material for arabic vocabulary with augmented reality technology

This research focuses on the production and creation of educational materials for Arabic vocabulary learning using Augmented Reality Technology, namely ARabic-Kafa, which can support both teachers and students. The Marker Based Tracking tool is used in this Augmented Reality application. This method was employed because it can read objects and then display 3-dimensional (3D) virtual objects. The first step in resolving this issue is to create animations through the blender application, choose the image to be utilised as a Marker, and finally, export it to Vuforia to form an Augmented Reality app. The mobile app of Augmented Reality-based educational media is the product of this research. A series of testing were employed and a minimum distance for displaying a 3D object is 10 cm, and the minimum angle 30° to 90° to view 3D objects. The rotation marker's output effectively shows every aspect of the 3D object. Furthermore, the audio pronunciations can be played effortlessly.
On Using RSA/ECC Coprocessor for Ideal Lattice-Based Key Exchange

Polynomial multiplication is one of the most costly operations of ideal lattice-based cryptosystems. In this work, we study its optimizations when one of the operands has coefficients close to 0. We focus on this structure since it is at the core of lattice-based Key Encapsulation Mechanisms submitted to the NIST call for post-quantum cryptography. In particular, we propose optimization of this operation for embedded devices by using a RSA/ECC coprocessor that provides efficient and secure large-integer arithmetic. In this context, we compare Kronecker Substitution, already studied in [], with two specific algorithms that we introduce: KSV, a variant of this substitution, and an adaptation of the schoolbook multiplication, denoted Shift&Add. All these algorithms rely on the transformation of polynomial multiplication to large-integer arithmetic. Then, thanks to these algorithms, existing secure coprocessors dedicated to large-integer can be re-purposed in order to speed-up post-quantum schemes. The efficiency of these algorithms depends on the component specifications and the cryptosystem parameters set. Thus, we establish a methodology to determine which algorithm to use, for a given component, by only implementing basic large-integer operations. Moreover, the three algorithms are assessed on a chip ensuring that the theoretical methodology matches with practical results.
Approximation capabilities of neural networks on unbounded domains

There is limited study in the literature on the representability of neural networks on unbounded domains. For some application areas, results in this direction provide additional value in the design of learning systems. Motivated by an old option pricing problem, we are led to the study of this subject. For networks with a single hidden layer, we show that under suitable conditions they are capable of universal approximation in Lp(R×[0,1]n) but not in Lp(R2×[0,1]n). For deeper networks, we prove that the ReLU network with two hidden layers is a universal approximator in Lp(Rn).
Scientific infrastructure of social sciences and humanities in European Union

The concept of scientific infrastructure in the interpretation adopted in the European Union is considered. It is indicated that this is a modern form of organization of scientific information services. The structure of European organizations and projects forming the scientific infrastructure of European organizations is described. The activities of European consortia of scientific infrastructure of social and humanitarian profile, including information resources and services implemented in these consortia, are described in particular detail. It is concluded that the European experience can be very useful for the development of scientific information services in Russia.
Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications

Manufacturers can plan predictive maintenance by remotely monitoring their assets. However, to extract the necessary insights from monitoring data, they often lack sufficiently large datasets that are labeled by human experts. We suggest combining knowledge-driven and unsupervised data-driven approaches to tackle this issue. Additionally, we present a dynamic dashboard that automatically visualizes detected events using semantic reasoning, assisting experts in the revision and correction of event labels. Captured label corrections are immediately fed back to the adaptive event detectors, improving their performance. To the best of our knowledge, we are the first to demonstrate the synergy of knowledge-driven detectors, data-driven detectors and automatic dashboards capturing feedback. This synergy allows a transition from detecting only unlabeled events, such as anomalies, at the start to detecting labeled events, such as faults, with meaningful descriptions. We demonstrate all work using a ventilation unit monitoring use case. This approach enables manufacturers to collect labeled data for refining event classification techniques with reduced human labeling effort.
The Four Ways to Consider Measurement Noise in Bayesian Model Selection—And Which One to Choose

Abstract Bayesian model selection (BMS) is a statistically rigorous approach to assess the plausibility of competing models. It naturally accounts for uncertainties in models and data. In this study, we discuss the role of measurement noise in BMS deeper than in past literature. We distinguish between four cases, accounting for noise in models and/or data: (1) no‐no, (2) no‐yes, (3) yes‐no, and (4) yes‐yes. These cases differ mathematically and philosophically. Only two out of these four cases are logically consistent, and they represent two potentially conflicting research questions: “Which model is best in modeling the pure physics?” (Case 1) and “which model is best in predicting the data‐generating process (i.e., physics plus noise)?” (Case 4). If we are interested in the “pure physics question,” we face two practical challenges: First, we would need noise‐free data, which is impossible to obtain; and second, the numerical approximation of Bayesian model evidence can be hard when neglecting noise. We discuss how to address both challenges and reveal that a fallback to the easier “data‐generation question” as a proxy for the “physics question” is not appropriate. We demonstrate on synthetic scenarios and a real‐world hydrogeological case study that the choice of the case has a significant impact on the outcome of posterior model weights, and hence on results of the model ranking, model selection, model averaging, model confusion analysis, and uncertainty quantification. Reality might force us to use a different case than philosophy would suggest, and we provide guidance on how to interpret model probabilities under such conditions.
Review on path planning algorithm for unmanned aerial vehicles

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.
Correction to: Co-active neuro-fuzzy inference system model as single imputation approach for non-monotone pattern of missing data

This paper is an addendum to Ref. Neural Computing and Applications (2021) 33:8981–9004 that adds information to clarify certain aspects of the models used adding information and some citations. In this paper an approach based on Co-active Neuro-Fuzzy Inference System named CANFIS-ART is proposed to automate data imputation procedure. This model is compared to other state-of-the-art imputation techniques such as its baseline model Artificial Neural Network and statistical methods, using a total of eighteen databases exposed to a perturbation procedure based on the random generation of non-monotone missing values pattern. A comparison of databases imputed by these models using a set of three classifiers were conducted.
AudioVisual Video Summarization

Audio and vision are two main modalities in video data. Multimodal learning, especially for audiovisual learning, has drawn considerable attention recently, which can boost the performance of various computer vision tasks. However, in video summarization, most existing approaches just exploit the visual information while neglecting the audio information. In this brief, we argue that the audio modality can assist vision modality to better understand the video content and structure and further benefit the summarization process. Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task and develop an audiovisual recurrent network (AVRN) to achieve this. Specifically, the proposed AVRN can be separated into three parts: 1) the two-stream long-short term memory (LSTM) is used to encode the audio and visual feature sequentially by capturing their temporal dependency; 2) the audiovisual fusion LSTM is used to fuse the two modalities by exploring the latent consistency between them; and 3) the self-attention video encoder is adopted to capture the global dependency in the video. Finally, the fused audiovisual information and the integrated temporal and global dependencies are jointly used to predict the video summary. Practically, the experimental results on the two benchmarks, i.e., SumMe and TVsum, have demonstrated the effectiveness of each part and the superiority of AVRN compared with those approaches just exploiting visual information for video summarization.
Fast &amp; Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems

This paper develops a new class of algorithms for general linear systems and eigenvalue problems. These algorithms apply fast randomized sketching to accelerate subspace projection methods, such as GMRES and Rayleigh--Ritz. This approach offers great flexibility in designing the basis for the approximation subspace, which can improve scalability in many computational environments. The resulting algorithms outperform the classic methods with minimal loss of accuracy. For model problems, numerical experiments show large advantages over MATLAB's optimized routines, including a $100 \times$ speedup over gmres and a $10 \times$ speedup over eigs.
Contextualizing benefits and limitations reported for augmented reality in construction research

Augmented Reality (AR) has been in use for years, and is increasingly being adopted in the construction industry. Research has explored various applications of AR in construction to identify its benefits and drawbacks. To synthesize these findings, prior works have conducted literature reviews about AR in construction, but these papers generally focus on identifying attributes for which there is consensus about AR benefits or drawbacks. While this is important, it highlights the need to identify trends in AR literature for which there is divergence in reporting to better understand the underlying contextual factors that may impact the success of AR implementation. This paper investigates trends in AR literature by studying both benefits and limitations reported when using current generation AR devices for construction applications. This is done by reviewing 49 articles from 2013 to 2020 found through a key word search for “augmented reality”, “mixed reality” and “construction”. The papers are categorized according to the most common attributes reported. The benefits and limitations identified are analysed based on how the technology was used. For several AR attributes, including ease of implementation, learning/training time, field of view, hardware and software performance, occlusion and immersiveness, there was consensus in reporting among researchers. For other AR attributes, including cost, cognitive performance, development time, tracking and registration, attitude towards the technology, and efficiency, there was divergence in reports among researchers. For each of these divergent attributes, the authors provide a discussion related to the contextual factors that were present. Beyond illustrating that some attributes do not lead to agreement between researchers, the results also indicate contextual factors that may contribute to the difference in reports. These findings contribute to the literature by enabling researchers to include or exclude contextual factors to gain or mitigate previously reported benefits or challenges when using AR in construction.
A Cross-cultural Investigation on Attitudes Towards Social Robots: Iranian and Chinese University Students

A Cross-cultural Investigation on Attitudes Towards Social Robots: Iranian and Chinese University Students
On Bayesian estimation of densities and sampling distributions: The posterior predictive distribution as the Bayes estimator

Optimality results for two outstanding Bayesian estimation problems are given in this paper: the estimation of the sampling distribution for the squared total variation function and the estimation of the density for the $L^1$-squared loss function. The posterior predictive distribution provides the solution to these problems. Some examples are presented to illustrate it. The Bayesian estimation problem of a distribution function is also addressed. Consistency of the estimator of the density is proved.
American Sign Language Recognition using Convolutional Neural Network

There have been a lot of research and advancements for helping people who cannot speak and listen with the help of modern technology. People like that can't even utilize voice applications like Google assistant, Siri etc. American Sign Language (ASL) is such complete language which contains various signs using hand movements for dumb & deaf people. The main focus of this paper is on using Deep Learning methodologies to analyze the sign language symbols and make useful predictions accordingly. Sign language is not known by many people so it will help the people who are deaf and dumb by analyzing the common trends from data and predict the best result. This way, we can handle the situation in a better manner. Deep learning, convolutional neural network, computer vision, Tensorflow etc. modern techniques along with python programming language are being used in this paper to build an application that will detect hand signs and will give accurate and appropriate result. Dataset used in this project is containing static hand gestures images captured with the help of webcam and then preprocessed to feed them to the model making part. The resultant application does live detection using camera feed and give result about what sign means shown by the user. Future scope is also high of this project as it can also be extended to detect even vast multitude of hand gestures, words or even sentences.
Toward Safer Navigation of Heterogeneous Mobile Robots in Distributed Scheme: A Novel Time-to-Collision-Based Method

For safe and efficient navigation of heterogeneous multiple mobile robots (HMRs), it is essential to incorporate dynamics (mass and inertia) in motion control algorithms. Many methods rely only on kinematics or point-mass models, resulting in conservative results or occasionally failure. This is especially true for robots with different masses. In this article, we develop a novel navigation methodology for a distributed scheme by incorporating the robots’ dynamics through calculating the time to collision (TTC) and designing a new controller accordingly that avoids collisions. We first propose a new predictive collision term by TTC that will be used to quantify imminent collisions among HMRs. Subsequently, using this term, we develop a novel nonlinear controller that explicitly incorporates TTC in the design and guarantees collision-free motion. Simulations and experiments were performed to demonstrate the effectiveness of the developed methods. We first compared the results of our proposed approach with controllers that only consider the robots’ kinematics. It was shown that the proposed control strategy (a TTC-based controller) proves to be less conservative when determining safe motions. Specifically, for environments with limited space, it was demonstrated that using robots’ kinematics may result in a collision, while our strategy results in safe motion. We also performed experiments that proved collision-free navigation of HMRs with this approach. The outcomes of this work provide more reliable motion control for HMRs, especially when the robots’ masses or inertias are significantly different, for example, warehouses. The developments in this work are also applicable to vehicles and can therefore be beneficial in automated collision avoidance in autonomous driving and intelligent transportation.
Improving Aleatoric Uncertainty Quantification in Multi-annotated Medical Image Segmentation with Normalizing Flows

Quantifying uncertainty in medical image segmentation applications is essential, as it is often connected to vital decision-making. Compelling attempts have been made in quantifying the uncertainty in image segmentation architectures, e.g. to learn a density segmentation model conditioned on the input image. Typical work in this field restricts these learnt densities to be strictly Gaussian. In this paper, we propose to use a more flexible approach by introducing Normalizing Flows (NFs), which enables the learnt densities to be more complex and facilitate more accurate modeling for uncertainty. We prove this hypothesis by adopting the Probabilistic U-Net and augmenting the posterior density with an NF, allowing it to be more expressive. Our qualitative as well as quantitative (GED and IoU) evaluations on the multi-annotated and single-annotated LIDC-IDRI and Kvasir-SEG segmentation datasets, respectively, show a clear improvement. This is mostly apparent in the quantification of aleatoric uncertainty and the increased predictive performance of up to 14%. This result strongly indicates that a more flexible density model should be seriously considered in architectures that attempt to capture segmentation ambiguity through density modeling. The benefit of this improved modeling will increase human confidence in annotation and segmentation, and enable eager adoption of the technology in practice.
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification

We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into a small size of 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label). The resulting dataset, consisting of 708,069 2D images and 10,214 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision, and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at https://medmnist.com/.
Influence of Emojis on Online Trust Among College Students

Emojis are increasingly used in online communication and expression, however, most previous studies have focused on describing this phenomenon, but less on how it affects interpersonal trust relationships. Therefore, this study examines the effect of emojis on online interpersonal trust among college students through three experiments. A total of 62 college students were recruited for Experiment 1. The results demonstrated that positive emoji () improved the level of trust of trustors in the trust game [t(60) = -2.79, p = 0.007], whereas that of the control group exerted no effect on the initial level of online trust among college students. Then, 74 college students were selected for Experiment 2. The results indicated no significant differences between the experiment and control groups in terms of the influence of negative emojis () on initial online trust using. A joint analysis (via ANOVA) of Experiments 1 and 2 illustrated that the type of emoji exerted a significant effect [F(2,96) = 3.96, p = 0.02, η 2 = 0.08] on college students' online trust. Finally, we recruited 111 participants for Experiment 3 to explore the role of emojis on online trust among acquaintances. The results suggested that the individual propensity to trust plays a moderate role in the relationship between emojis and online trust among acquaintances. That is, emojis influenced interpersonal trust among acquaintances only if the level of propensity to trust, is low.
A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

Facial expression is one of the important ways of transferring emotion in interpersonal communication, and it has been widely used in many interpersonal communication systems. The traditional facial expression recognition methods are not intelligent enough to manage the model uncertainty. The deep learning method has obvious ability to deal with model uncertainty in the image recognition. The deep learning method is able to complete the facial expression work, but the recognition rate can be further improved by a hybrid learning strategy. In this paper, a Fuzzy optimized convolutional neural network-recurrent neural network (CNN-RNN) method for facial expression recognition is proposed to solve the problems of direct image convolution without image enhancement and simple convolution stack ignoring feature layer-by-layer convolution resulting in information loss. Firstly, each face image is scaled by the bilinear interpolation and the affine transformation is adopted to expand the image data to avoid the shortage of the data set. Then the feature map of the facial expression is extracted by the CNN with small information loss. To deal with the uncertainty in the feature map, the Fuzzy logic is employed to reduce the uncertainty by recognizing the highly nonlinear relationship between the features. Then the output of the Fuzzy model is fed with the RNN to classify different facial expression images. The recognition results based on the open datasets CK, Jaffe, and FER2013 show that the proposed Fuzzy optimized CNN-RNN method has a certain improvement in the recognition effect of different facial expression data sets compared with current popular algorithms.