Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilingual Toxic Comment Classiﬁcation

Freedom of speech on the Internet has also led to a pervasive presence of toxic comments in online discussions and a need to detect their presence in any language. While previous work focused on classiﬁcation models for English input or other pre-deﬁned language input, we investigate whether English-trained toxic comment classiﬁcation models can be language agnostic. We trained 6 models on English inputs and validated against non-English inputs. We found that models do not perform exceptionally well non-English inputs, but the mislabeling errors was not evenly distributed across languages. This is likely due to degree of similarities between each of the non-English language and English. Based on the model performance, we conclude that existing English-only trained and non-English validated toxic classiﬁcation models likely will not perform well on non-English inputs.
Conceptual Modeling of Explainable Recommender Systems: An Ontological Formalization to Guide Their Design and Development

With the increasing importance of e-commerce and the immense variety of products, users need help to decide which ones are the most interesting to them. This is one of the main goals of recommender systems. However, users’ trust may be compromised if they do not understand how or why the recommendation was achieved. Here, explanations are essential to improve user confidence in recommender systems and to make the recommendation useful. Providing explanation capabilities into recommender systems is not an easy task as their success depends on several aspects such as the explanation’s goal, the user’s expectation, the knowledge available, or the presentation method. Therefore, this work proposes a conceptual model to alleviate this problem by defining the requirements of explanations for recommender systems. Our goal is to provide a model that guides the development of effective explanations for recommender systems as they are correctly designed and suited to the user’s needs. Although earlier explanation taxonomies sustain this work, our model includes new concepts not considered in previous works. Moreover, we make a novel contribution regarding the formalization of this model as an ontology that can be integrated into the development of proper explanations for recommender systems.
FE-MODELLING OF DYNAMIC BEHAVIOR OF SEISMIC

In this work, the behavior of the elastomeric material, used in the manufacture of isolators, was characterized. Data obtained from experimental uniaxial compression and tension tests were used to determine the parameters of the different hyperelastic models that are available in several commercial software of Finite Element analysis. Simulation of the test is compared with the experimental data. The constitutive models that best represent the behavior of the hyperelastic materials, were used in order to simulate the behavior of the isolator subjected to lateral and cyclic load; a good agreement is observed between numerical and experimental results. It is found that the Polynomial hyperelastic model, for the rubber used, presents a better approximation regarding the behavior of the elastomeric isolator.
Carbon and Boron Nitride nanostructures for Hydrogen storage applications through a theoretical perspective

The recent progress in the field of hydrogen storage in carbon and boron nitride nanostructures has been summarized. Carbon and boron nitride nanostructures are considered advantageous in this prospect due to their lightweight and high surface area. Demerits of pristine structures to hold hydrogen molecules for mobile applications have been highlighted by many researchers. In such cases, weak van der Waals interaction comes into account, hence, the hydrogen molecules are weakly bonded with the host materials and hence weak adsorption energy and low hydrogen molecules uptake. So, to tune the adsorption energy as well as overall kinetics, methods such as doping, light alkali-alkaline earth metals decoration, vacancy, functionalization, pressure variation, application of external electric field, and biaxial strain has been adopted by many researchers. Physisorption with atoms decoration is promising for hydrogen storage application. Under this condition, the host materials have high storage capacity with considerable average adsorption energy, feasible adsorption/desorption kinetics.
Toward a Better Understanding of Activation Volume and Dynamic Decoupling of Glass‐Forming Liquids under Compression

Anh D. Phan, 2, ∗ Nguyen K. Ngan, † Nam B. Le, and Le T. M. Thanh Faculty of Materials Science and Engineering, Phenikaa University, Hanoi 12116, Vietnam 2 Phenikaa Institute for Advanced Study, Phenikaa University, Hanoi 12116, Vietnam School of Engineering Physics, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi, Vietnam Faculty of Basic Science, Posts and Telecommunications Institute of Technology, 122 Hoang Quoc Viet, Hanoi 10000, Vietnam (Dated: July 29, 2021)
Evaluating the weight sensitivity in AHP-based flood risk estimation models

In the analytic hierarchy process (AHP) based flood risk estimation models, it is widely acknowledged that different weighting criteria can lead to different results. In this study, we evaluated and discussed the sensitivity of flood risk estimation brought by judgment matrix definition by investigating the performance of pixel-based and sub-watershed-based AHP models. Taking a flood event that occurred in July 2020 in Chaohu basin, Anhui province, China, as a study case, we used the flood areas extracted from remote sensing images to construct ground truth for validation purposes. The results suggest that the performance of the pixelbased AHP model fluctuates intensively given different definitions of judgment matrixes, while the performance of sub-watershed-based AHP models fluctuates considerably less than that of the pixel-based AHP model. Specifically, sub-watershed delimitated via multiple flow direction (MFD) always achieves increases in the correct ratio and the fit ratio by >35% and >5% with the pixel-based AHP model, respectively.
Marine Vehicles Localization Using Grid Cells for Path Integration

Autonomous Underwater Vehicles (AUVs) are platforms used for research and exploration of marine environments. However, these types of vehicles face many challenges that hinder their widespread use in the industry. One of the main limitations is obtaining accurate position estimation, due to the lack of GPS signal underwater. This estimation is usually done with Kalman filters. However, new developments in the neuroscience field have shed light on the mechanisms by which mammals are able to obtain a reliable estimation of their current position based on external and internal motion cues. A new type of neuron, called Grid cells, has been shown to be part of path integration system in the brain. In this article, we show how grid cells can be used for obtaining a position estimation of underwater vehicles. The model of grid cells used requires only the linear velocities together with heading orientation and provides a reliable estimation of the vehicle’s position. We provide simulation results for an AUV which show the feasibility of our proposed methodology.
Sharp superlevel set estimates for small cap decouplings of the parabola

We prove sharp bounds for the size of superlevel sets {x ∈ R : |f(x)| > α} where α > 0 and f : R → C is a Schwartz function with Fourier transform supported in an R-neighborhood of the truncated parabola P. These estimates imply the small cap decoupling theorem for P from [DGW20] and the canonical decoupling theorem for P from [BD15]. New (l, L) small cap decoupling inequalities also follow from our sharp level set estimates. In this paper, we further develop the high/low frequency proof of decoupling for the parabola [GMW20] to prove sharp level set estimates which recover and refine the small cap decoupling results for the parabola in [DGW20]. We begin by describing the problem and our results in terms of exponential sums. The main results in full generality are in §1. For N ≥ 1, R ∈ [N,N2], and 2 ≤ p, let D(N,R, p) denote the smallest constant so that
Growing knowledge culturally across generations to solve novel, complex tasks

Knowledge built culturally across generations allows humans to learn far more than an individual could glean from their own experience in a lifetime. Cultural knowledge in turn rests on language: language is the richest record of what previous generations believed, valued, and practiced. The power and mechanisms of language as a means of cultural learning, however, are not well understood. We take a ﬁrst step towards reverse-engineering cultural learning through language. We developed a suite of complex high-stakes tasks in the form of minimalist-style video games, which we deployed in an iterated learning paradigm. Game participants were limited to only two attempts (two lives) to beat each game and were allowed to write a message to a future participant who read the message before playing. Knowledge accumulated gradually across generations, allowing later generations to advance further in the games and perform more efﬁcient actions. Multigenerational learning followed a strikingly similar trajectory to individuals learning alone with an unlimited number of lives. These results suggest that language provides a sufﬁcient medium to express and accumulate the knowledge people acquire in these diverse tasks: the dynamics of the environment, valuable goals, dangerous risks, and strategies for success. The video game paradigm we pioneer here is thus a rich test bed for theories of cultural transmission and learning from language.
5G Multi-access Edge Computing: Security, Dependability, and Performance

—The main innovation of the Fifth Generation (5G) of mobile networks is the ability to provide novel services with new and stricter requirements. One of the technologies that enable the new 5G services is the Multi-access Edge Computing (MEC). MEC is a system composed of multiple devices with computing and storage capabilities that are deployed at the edge of the network, i.e., close to the end users. MEC reduces latency and enables contextual information and real-time awareness of the local environment. MEC also allows cloud ofﬂoading and the reduction of trafﬁc congestion. Performance is not the only requirement that the new 5G services have. New mission-critical applications also require high security and dependability. These three aspects (security, dependability, and performance) are rarely addressed together. This survey ﬁlls this gap and presents 5G MEC by addressing all these three aspects. First, we overview the background knowledge on MEC by referring to the current standardization efforts. Second, we individually present each aspect by introducing the related taxonomy (important for the not expert on the aspect), the state of the art, and the challenges on 5G MEC. Finally, we discuss the challenges of jointly addressing the three aspects.
Nonlinear mode-tide coupling in coalescing binary neutron stars with relativistic corrections

We compute the internal modes of a nonspinning neutron star and its tidal metric perturbation in general relativity, and determine the effect of relativistic corrections to the modes on mode coupling. Claims have been made that a new hydrodynamic instability can occur in a neutron star in a binary neutron star system triggered by the nonlinear coupling of the companion's tidal field to pairs of p-modes and g-modes in it as the binary inspirals toward merger. This"PG"instability may be significant since it can influence the binary's inspiral phase by extracting orbital energy, thereby potentially causing large deviations in their gravitational waveforms from those predicted by theoretical models that do not account for it. This can result in incorrect parameter estimation, at best, or mergers going undetected, at worst, owing to the use of deficient waveform models. On the other hand, better modeling of this instability and its effect on binary orbits can unravel a new phenomenon and shed light on stellar instabilities, via gravitational wave observations. So far, all mode-tide coupling instability studies have been formulated in Newtonian perturbation theory. Neutron stars are compact objects, so relativistic corrections might be important. We present and test a new code to calculate the relativistic eigenmodes of nonrotating relativistic stars. We use these relativistic tide and neutron star eigenmodes to compute the mode-tide coupling strength (MTCS) for a few selected equations of state. The MTCS thus calculated can be at most tens of percent different from its purely Newtonian value, but we confirm the dependencies on orbital separation and equation of state found by Newtonian calculations. For some equations of state, the MTCS is very sensitive to the neutron star crust region, demonstrating the importance of treating this region accurately.
The Nuclear Star Cluster and Nuclear Stellar Disk of the Milky Way: Different Stellar Populations and Star Formation Histories

The Milky Way’s nuclear stellar disk (NSD) and nuclear star cluster (NSC) are the main features of the Galactic center. Nevertheless, their observation is hampered by the extreme source crowding and high extinction. Hence, their relation and formation scenario are not fully clear yet. We aim to detect the stellar populations from the NSC and the NSD along the line of sight toward the NSC and assess whether they have different stellar populations and star formation histories. We analyzed the color–magnitude diagram, K s versus H−K s , of a region of 8. ′2 × 2. ′8 centered on the NSC, and detected two different stellar groups with different extinctions. We studied their red clumps to find the features associated with each of the stellar populations. We obtained that the two groups of stars correspond to the NSD and the NSC and found that they have significantly different stellar populations and star formation histories. We detected a double red clump for the NSD population, in agreement with previous work, whereas the NSC presents a more complex structure well fitted by three Gaussian features. We created extinction maps to analyze the extinction variation between the detected stellar groups. We found that the high-extinction layer varies on smaller scales (arcseconds) and that there is a difference of AKs ∼ 0.6 mag between both extinction layers. Finally, we obtained that the distance toward each of the stellar populations is compatible with the Galactic center distance and found some evidence of a slightly closer distance for the NSD stars (∼360 ± 200 pc).
On the State of Reporting in Crowdsourcing Experiments and a Checklist to Aid Current Practices

Crowdsourcing is being increasingly adopted as a platform to run studies with human subjects. Running a crowdsourcing experiment involves several choices and strategies to successfully port an experimental design into an otherwise uncontrolled research environment, e.g., sampling crowd workers, mapping experimental conditions to micro-tasks, or ensure quality contributions. While several guidelines inform researchers in these choices, guidance of how and what to report from crowdsourcing experiments has been largely overlooked. If under-reported, implementation choices constitute variability sources that can affect the experiment's reproducibility and prevent a fair assessment of research outcomes. In this paper, we examine the current state of reporting of crowdsourcing experiments and offer guidance to address associated reporting issues. We start by identifying sensible implementation choices, relying on existing literature and interviews with experts, to then extensively analyze the reporting of 171 crowdsourcing experiments. Informed by this process, we propose a checklist for reporting crowdsourcing experiments.
XFL: eXtreme Function Labeling

—Reverse engineers would beneﬁt from identiﬁers like function names, but these are usually unavailable in binaries. Training a machine learning model to predict function names automatically is promising but fundamentally hard due to the enormous number of classes. In this paper, we introduce eXtreme Function Labeling (XFL), an extreme multi-label learning approach to selecting appropriate labels for binary functions. XFL splits function names into tokens, treating each as an informative label akin to the problem of tagging texts in natural language. To capture the semantics of binary code, we introduce D EXTER , a novel function embedding that combines static analysis-based features with local context from the call graph and global context from the entire binary. We demonstrate that XFL outperforms state-of-the-art approaches to function labeling on a dataset of over 10,000 binaries from the Debian project, achieving a precision of 82.5%. We also study combinations of XFL with different published embeddings for binary functions and show that D EXTER consistently improves over the state of the art in information gain. As a result, we are able to show that binary function labeling is best phrased in terms of multi-label learning, and that binary function embeddings beneﬁt from moving beyond just learning from syntax.
Toward AI Assistants That Let Designers Design

AI for supporting designers needs to be rethought. It should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We present AI-assisted design: a framework for creating such AI, built around generative user models which enable reasoning about designers' goals, reasoning, and capabilities.
Is Object Detection Necessary for Human-Object Interaction Recognition?

This paper revisits human-object interaction (HOI) recognition at image level without using supervisions of object location and human pose. We name it detection-free HOI recognition, in contrast to the existing detection-supervised approaches which rely on object and keypoint detections to achieve state of the art. With our method, not only the detection supervision is evitable, but superior performance can be achieved by properly using image-text pre-training (such as CLIP) and the proposed Log-Sum-Exp Sign (LSE-Sign) loss function. Specifically, using text embeddings of class labels to initialize the linear classifier is essential for leveraging the CLIP pre-trained image encoder. In addition, LSE-Sign loss facilitates learning from multiple labels on an imbalanced dataset by normalizing gradients over all classes in a softmax format. Surprisingly, our detection-free solution achieves 60.5 mAP on the HICO dataset, outperforming the detection-supervised state of the art by 13.4 mAP
A Thorough Review on Recent Deep Learning Methodologies for Image Captioning

Image Captioning is a task that combines computer vision and natural language processing, where it aims to generate descriptive legends for images. It is a two-fold process relying on accurate image understanding and correct language understanding both syntactically and semantically. It is becoming increasingly difficult to keep up with the latest research and findings in the field of image captioning due to the growing amount of knowledge available on the topic. There is not, however, enough coverage of those findings in the available review papers. We perform in this paper a run-through of the current techniques, datasets, benchmarks and evaluation metrics used in image captioning. The current research on the field is mostly focused on deep learning-based methods, where attention mechanisms along with deep reinforcement and adversarial learning appear to be in the forefront of this research topic. In this paper, we review recent methodologies such as UpDown, OSCAR, VIVO, Meta Learning and a model that uses conditional generative adversarial nets. Although the GAN-based model achieves the highest score, UpDown represents an important basis for image captioning and OSCAR and VIVO are more useful as they use novel object captioning. This review paper serves as a roadmap for researchers to keep up to date with the latest contributions made in the field of image caption generation.
Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning

Drug-target interaction (DTI) prediction plays a crucial role in drug discovery, and deep learning approaches have achieved state-of-the-art performance in this field. We introduce an ensemble of deep learning models (EnsembleDLM) for DTI prediction. EnsembleDLM only uses the sequence information of chemical compounds and proteins, and it aggregates the predictions from multiple deep neural networks. This approach not only achieves state-of-the-art performance in Davis and KIBA datasets but also reaches cutting-edge performance in the cross-domain applications across different bio-activity types and different protein classes. We also demonstrate that EnsembleDLM achieves a good performance (Pearson correlation coefficient and concordance index $\gt 0.8)$ in the new domain while the training set has twice as much data as the test set with transfer learning.
Both partners practicing orgasmic meditation report having a mystical-type experience: results using the Mystical Experience Questionnaire

Background: Practitioners in a variety of spiritual/religious traditions have described “mystical experiences”, defined by a common set of qualities. The “Mystical Experience Questionnaire” (MEQ30) provides a validated and quantitative measure of mystical experience, and has been used successfully to demonstrate that the hallucinogenic substance psilocybin triggers a mystical-type experience. Orgasmic Meditation (OM) is a structured, partnered meditative practice involving manual stimulation of the clitoris. Although the partners in an OM have different roles (one is stroking, and the other is being stroked), both claim benefit from the practice. The aim of the current study is to use the MEQ30 to assess to what extent participants report mystical experiences during OM, and to what extent that experience is correlated between the partners. Methods: In Study 1, 780 participants completed the MEQ30 with a single powerful OM in mind. In Study 2, 56 pairs of participants (both partners) completed the MEQ30 after their next OM. If the respondent had a score ≥60% of the maximum possible score on each of the four subscales of the MEQ30, this was considered a “complete” mystical experience. Results: Respondents from Study 1 reported an MEQ total score of 3.35 (SD = 1.08), with 62% of respondents reporting a complete mystical experience. Respondents from Study 2 reported an MEQ total score of 3.21 (SD = 0.92), with 23% reporting a complete mystical experience. We found strong relationships between MEQ total score and role (i.e., stroker or strokee), interrater agreement within-group index (aWG) = 0.46, and an even stronger relationship between partners and MEQ total score, aWG=0.71. Conclusions: These findings suggest that OM can trigger a substantial mystical experience in both partners. Whether the brains of people who OM show similar activity changes to those having other mystical experiences awaits future study.