Reviewerly/exHarmony huggingface dataset:
ConTrOn: Continuously Trained Ontology based on Technical Data Sheets and Wikidata

In engineering projects involving various parts from global suppliers, one common task is to determine which parts are best suited for the project requirements. Information about specific parts' characteristics is published in so called data sheets. However, these data sheets are oftentimes only published in textual form, e.g., as a PDF. Hence, they have to be transformed into a machine-interpretable format. This transformation process still requires a lot of manual intervention and is prone to errors. Automated approaches make use of ontologies to capture the given domain and thus improve automated information extraction from the data sheets. However, ontologies rely solely on experiences and perspectives of their creators at the time of creation and cannot accumulate knowledge over time on their own. This paper presents ConTrOn -- Continuously Trained Ontology -- a system that automatically augments ontologies. ConTrOn tackles terminology problems by combining the knowledge extracted from data sheets with an ontology created by domain experts and external knowledge bases such as WordNet and Wikidata. To demonstrate how the enriched ontology can improve the information extraction process, we selected data sheets from spacecraft development as a use case. The evaluation results show that the amount of information extracted from data sheets based on ontologies is significantly increased after the ontology enrichment.
Towards Task and Architecture-Independent Generalization Gap Predictors

Can we use deep learning to predict when deep learning works? Our results suggest the affirmative. We created a dataset by training 13,500 neural networks with different architectures, on different variations of spiral datasets, and using different optimization parameters. We used this dataset to train task-independent and architecture-independent generalization gap predictors for those neural networks. We extend Jiang et al. (2018) to also use DNNs and RNNs and show that they outperform the linear model, obtaining $R^2=0.965$. We also show results for architecture-independent, task-independent, and out-of-distribution generalization gap prediction tasks. Both DNNs and RNNs consistently and significantly outperform linear models, with RNNs obtaining $R^2=0.584$.
Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions using Similarity Domains Network

We demonstrate the use of similarity domains (SDs) for shape modeling and skeleton extraction. SDs are recently proposed and they can be utilized in a neural network framework to help us analyze shapes. SDs are modeled with radial basis functions with varying shape parameters in Similarity Domains Networks (SDNs). In this paper, we demonstrate how using SDN can first help us model a pixel-based image in terms of SDs and then demonstrate how those learned SDs can be used to extract the skeleton of a shape.
Designing for Emergent Security in Heterogeneous Human-Machine Teams

This work seeks to design decisionmaking rules for autonomous agents to jointly influence and optimize the behavior of teamed human decisionmakers in the presence of an adversary. We study a situation in which computational jobs are scheduled on servers by a collection of autonomous machines in concert with self-interested human decisionmakers, and the human and machine schedulers must react to an adversary's attack on one of the servers. We show a simple machine scheduling policy such that if all schedulers have permission to schedule jobs on all servers, increasing the penetration of machine schedulers always increases the level of security in the system, even when the machine schedulers have no explicit coordination or communication amongst themselves. However, we show a companion result in which simple constraints on server availability can nullify the machine schedulers' ability to effectively influence human schedulers; here, even if machine schedulers control an overwhelming majority of jobs, are socially-aware, and fully coordinated amongst themselves, they are incapable of influencing human decisionmakers to mitigate the harm of an attack.
Perceptual Values from Observation

Imitation by observation is an approach for learning from expert demonstrations that lack action information, such as videos. Recent approaches to this problem can be placed into two broad categories: training dynamics models that aim to predict the actions taken between states, and learning rewards or features for computing them for Reinforcement Learning (RL). In this paper, we introduce a novel approach that learns values, rather than rewards, directly from observations. We show that by using values, we can significantly speed up RL by removing the need to bootstrap action-values, as compared to sparse-reward specifications.
Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning

We consider distributed gradient descent in the presence of stragglers. Recent work on \em gradient coding \em and \em approximate gradient coding \em have shown how to add redundancy in distributed gradient descent to guarantee convergence even if some workers are \em stragglers\em---that is, slow or non-responsive. In this work we propose an approximate gradient coding scheme called \em Stochastic Gradient Coding \em (SGC), which works when the stragglers are random. SGC distributes data points redundantly to workers according to a pair-wise balanced design, and then simply ignores the stragglers. We prove that the convergence rate of SGC mirrors that of batched Stochastic Gradient Descent (SGD) for the $\ell_2$ loss function, and show how the convergence rate can improve with the redundancy. We also provide bounds for more general convex loss functions. We show empirically that SGC requires a small amount of redundancy to handle a large number of stragglers and that it can outperform existing approximate gradient codes when the number of stragglers is large.
Matlab vs. OpenCV: A Comparative Study of Different Machine Learning Algorithms

Scientific Computing relies on executing computer algorithms coded in some programming languages. Given a particular available hardware, algorithms speed is a crucial factor. There are many scientific computing environments used to code such algorithms. Matlab is one of the most tremendously successful and widespread scientific computing environments that is rich of toolboxes, libraries, and data visualization tools. OpenCV is a (C++)-based library written primarily for Computer Vision and its related areas. This paper presents a comparative study using 20 different real datasets to compare the speed of Matlab and OpenCV for some Machine Learning algorithms. Although Matlab is more convenient in developing and data presentation, OpenCV is much faster in execution, where the speed ratio reaches more than 80 in some cases. The best of two worlds can be achieved by exploring using Matlab or similar environments to select the most successful algorithm; then, implementing the selected algorithm using OpenCV or similar environments to gain a speed factor.
Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling

We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. It is governed by a simple generative model, where the likelihood to generate the training documents via topics is optimized. The algorithm starts with one-word topics and joins two topics at every step. It therefore generates a solution for every desired number of topics ranging between the size of the training vocabulary and one. The process represents an agglomerative clustering that corresponds to a binary tree of topics. A resulting tree may act as a containment hierarchy, typically with more general topics towards the root of tree and more specific topics towards the leaves. Topic Grouper is not governed by a background distribution such as the Dirichlet and avoids hyper parameter optimizations. We show that Topic Grouper has reasonable predictive power and also a reasonable theoretical and practical complexity. Topic Grouper can deal well with stop words and function words and tends to push them into their own topics. Also, it can handle topic distributions, where some topics are more frequent than others. We present typical examples of computed topics from evaluation datasets, where topics appear conclusive and coherent. In this context, the fact that each word belongs to exactly one topic is not a major limitation; in some scenarios this can even be a genuine advantage, e.g.~a related shopping basket analysis may aid in optimizing groupings of articles in sales catalogs.
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking

Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on the estimated 2D-pose. We show that CVAE-based 3D-pose sample set is consistent with the 2D-pose and helps tackling the inherent ambiguity in 2D-to-3D lifting. We propose two strategies for obtaining the final 3D pose- (a) depth-ordering/ordinal relations to score and weight-average the candidate 3D-poses, referred to as OrdinalScore, and (b) with supervision from an Oracle. We report close to state of-the-art results on two benchmark datasets using OrdinalScore, and state-of-the-art results using the Oracle. We also show that our pipeline yields competitive results without paired image-to-3D annotations. The training and evaluation code is available at https://github.com/ssfootball04/generative_pose.
A set of $4d-3$ observables to determine any pure qudit state

We present a tomographic method which requires only $4d-3$ measurement outcomes to reconstruct \emph{any} pure quantum state of arbitrary dimension $d$. Using the proposed scheme we have experimentally reconstructed a large number of pure states of dimension $d=7$, obtaining a mean fidelity of $0.94$. Moreover, we performed numerical simulations of the reconstruction process, verifying the feasibility of the method for higher dimensions. In addition, the \emph{a priori} assumption of purity can be certified within the same set of measurements, what represents an improvement with respect to other similar methods and contributes to answer the question of how many observables are needed to uniquely determine any pure state.
Embracing OneNote as an Online Pedagogy

The COVID-19 pandemic has forced institutions worldwide shifting to fully online learning along with the campus shut down to avoid the spread of virus in year 2021. Considering the necessity of distance education during the pandemic, the Internet of Thing (IoT) and technology play an important role in keeping all connected. In this study, the usability and adoption of Microsoft OneNote class notebook via MS Teams are presented. In view of mathematics classes require a lot of annotation and demonstration of calculation solutions, OneNote class notebook via MS Teams is adopted due to its built-in content library space and collaboration space which act as the digital whiteboard for teachers and students. Besides, OneNote class notebook is able to provide a real-time synchronized space for all the mathematical works. The convenience, usefulness and real-time features of OneNote class notebook via MS Teams make it a powerful tool and as a substitute for the traditional classroom. The chat history and work done in the collaboration space during the break-out room sessions via MS Teams offer the students space to recall and retrieve what has been learned in the class. The COVID-19 crisis provides us an opportunity to blend in the component of IoT in education such as live and collaborative platforms in OneNote class notebook to complement both face-to-face and online education.
Advances in the automatic lemmatization of Old English: class V strong verbs (L-Y)

The grammatical description of Old English lacks complete and systematic lemmatization, which hinders Natural Language Processing studies in this language, as they strongly rely on the existence of large, annotated corpora. Moreover, the inflectional features of Old English preclude token-based automatic lemmatization. Therefore, specifically goal-oriented applications must be developed to account for the automatic lemmatization of specific variable categories. This article designs an automatic lemmatizer within the framework of Morphological Generation to address the type-based lemmatization of Old English class V strong verbs (L-Y). The lemmatizer is implemented with rules that account for inflectional, derivational and morphophonological variation. The generated forms are compared with the most relevant corpora of Old English for validation before being assigned a lemma. The lemmatizer is successful in supplying form-lemma associations not yet accounted for in the literature, and in identifying mismatches and areas for manual revision.
THE RELATIONSHIP OF DENTAL BRUSHING KNOWLEDGE WITH THE STATUS OF DENTAL AND MOUTH HYGIENE IN THE VIII CLASS ADOLESCENT OF SMPN 1 DARUL IMARAH ACEH BESAR, 2022

The maintenance of dental and oral health is often neglected by teenagers. Many bad habits of teenagers that can cause damage to the teeth and mouth, these bad habits include the habit of consuming sweet foods, drinking habits - sweet drinks and lazy habits of brushing your teeth at night. The results of the examination that the author conducted on 15 grade VIII students at SMPN 1 Darul Imarah, the average dental and oral hygiene was 3.3 with a poor category level. The results of interviews with 11 out of 15 students still did not know how to brush their teeth properly and correctly. The purpose of this study was to determine the relationship between knowledge of brushing teeth and the status of dental and oral hygiene in Class VIII Adolescents of SMPN 1 Darul Imarah Aceh Besar in 2022.This research is analytical with a cross sectional design, conducted at SMPN 1 Darul Imarah Aceh Besar District on 19-22 February 2022. The population in this study was 210 people. The sample in this study used a random sampling technique with a sample of 70 adolescents aged 13-14 years. Bivariate analysis looked at the relationship between knowledge of brushing teeth and oral hygiene status using the statistical test of 0.05.The results showed that the knowledge of brushing teeth with the most dental and oral hygiene status was in the moderate category as many as 20 people (28.6%), the knowledge of brushing teeth was not good with the most dental and oral hygiene status in the poor category as many as 23 people (32, 8%). It can be concluded that there is a relationship between knowledge of brushing teeth with dental and oral hygiene status in adolescents with a P Value: 0.01, it is recommended for adolescents to be more active in seeking information on social media about knowledge or maintenance of dental health such as brushing teeth properly and correctly so that avoid poor dental and oral hygiene status.
Construction of Sports Rehabilitation Training Method Based on Virtual Reality

The introduction of virtual reality technology into rehabilitation training can avoid various shortcomings of traditional rehabilitation training, and can efficiently complete training tasks. Based on the basic theory of virtual reality, this paper constructs a sports rehabilitation training method to achieve the consistency of the virtual upper limbs and the real upper limbs of the human body. The model provides a background environment in the virtual reality training, realizes the systematic maintenance of the patient’s training mode, provides the patient with a scientifically based training mode and evaluation results, solves the quantitative index problem of sports rehabilitation training, and builds a virtual upper limb platform at the same time. During the simulation process, the system used the Berg balance assessment scores to conduct four-stage assessments. Before treatment, the three-dimensional gait analysis, FMAL lower extremity function scores and Berg balance assessment results were not significantly different between the control group and the experimental group ([Formula: see text]). The experimental results showed that the three-dimensional gait analysis was used to analyze the pace, left and right step length, left and right support percentage, and stride frequency. Compared with the evaluation data before treatment, the index values of the control group and the experimental group were significantly improved ([Formula: see text]); after the treatment, the two groups of patients were evaluated for motor function, the evaluation values of the experimental group were higher than those of the control group when compared between the two groups. It effectively meets the human–computer interaction needs of rehabilitation training.
Image Feature Extraction and Retrieval Optimization of Book Pages Based on Convolutional Neural Network

Focusing on the feature extraction process of convolutional neural network (CNN), this paper establishes a CNN-based retrieval method of book pages. Then, the pretraining and feature finetuning of the CNN were described separately. The performance of the proposed optimization method was demonstrated through experiments. Considering overall performance and transfer learning capacity, the eight-layer VGG-Fast was selected as the structural framework of our CNN. To train the CNN, it is necessary to gather millions of book page images, and complete the complex task of labeling all these images. Given the excellence of VGG in many transfer learning tasks, this paper chooses to pretrain the CNN with a task-independent dataset. After that, a small book page dataset was adopted to convert the knowledge domain of the CNN from image classification to image page retrieval. In this way, desirable retrieval effects were achieved, without wasting lots of time and energy in collecting and labeling a large book page dataset.
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering

We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages scene graph structures to create 22M diverse reasoning questions, all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate question biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. An extensive analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%, offering ample opportunity for new research to explore. We strongly hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding for images and language.
Subsumption of Weakly Well-Designed SPARQL Patterns is Undecidable

Weakly well-designed SPARQL patterns is a recent generalisation of well-designed patterns, which preserve good computational properties but also capture almost all patterns that appear in practice. Subsumption is one of static analysis problems for SPARQL, along with equivalence and containment. In this paper we show that subsumption is undecidable for weakly well-designed patterns, which is in stark contrast to well-designed patterns, and to equivalence and containment.
Stroke-based sketched symbol reconstruction and segmentation

Hand-drawn objects usually consist of multiple semantically meaningful parts. For example, a stick figure consists of a head, a torso, and pairs of legs and arms. Efficient and accurate identification of these subparts promises to significantly improve algorithms for stylization, deformation, morphing and animation of 2D drawings. In this paper, we propose a neural network model that segments symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP) network that identifies a component based on the feature. As the feature extractor we utilize an encoder of a stroke-rnn, which is our newly proposed generative Variational Auto-Encoder (VAE) model that reconstructs symbols on a stroke by stroke basis. Experiments show that a single encoder could be reused for segmenting multiple categories of sketched symbols with negligible effects on segmentation accuracies. Our segmentation scores surpass existing methodologies on an available small state of the art dataset. Moreover, extensive evaluations on our newly annotated big dataset demonstrate that our framework obtains significantly better accuracies as compared to baseline models. We release the dataset to the community.
Assessment of the quality of distance (digital) education: analysis of the digital footprint of participants in the educational process

The article presents the results of research conducted to study the digital footprint and its capabilities for assessing the attitude of users to distance education. The sociological survey showed that the population is familiar with the concept of a digital footprint, but vaguely represents its content, methods of formation and areas of application. Intellectual analysis made it possible to assess their attitude to distance education based on the digital footprint left by users of social networks.
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis

As tools for content editing mature, and artificial intelligence (AI) based algorithms for synthesizing media grow, the presence of manipulated content across online media is increasing. This phenomenon causes the spread of misinformation, creating a greater need to distinguish between ``real'' and ``manipulated'' content. To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated). Many of the existing deepfake datasets focus exclusively on two types of facial manipulations -- swapping with a different subject's face or altering the existing face. VideoSham, on the other hand, contains more diverse, context-rich, and human-centric, high-resolution videos manipulated using a combination of 6 different spatial and temporal attacks. Our analysis shows that state-of-the-art manipulation detection algorithms only work for a few specific attacks and do not scale well on VideoSham. We performed a user study on Amazon Mechanical Turk with 1200 participants to understand if they can differentiate between the real and manipulated videos in VideoSham. Finally, we dig deeper into the strengths and weaknesses of performances by humans and SOTA-algorithms to identify gaps that need to be filled with better AI algorithms. We present the dataset at https://github.com/adobe-research/VideoSham-dataset.