Reviewerly/exHarmony huggingface dataset:
MARKER-LESS MOBILE AUGMENTED REALITY APPLICATION FOR MASSIVE 3D POINT CLOUDS AND SEMANTICS

Abstract. Mobile Augmented Reality (MAR) attracts significant research and development efforts from both the industry and academia, but rarely integrate massive 3D dataset’s interactions. The emergence of dedicated AR devices and powerful Software Development Kit (ARCore for android and ARKit for iOS) improves performance on mobile devices (Smartphones and tablets). This is aided by new sensor integration and advances in computer vision that fuels the development of MAR. In this paper, we propose a direct integration of massive 3D point clouds with semantics in a web-based marker-less mobile Augmented Reality (AR) application for real-time visualization. We specifically investigate challenges linked to point cloud data structure and semantic injection. Our solution consolidates some of the overarching principles of AR, of which pose estimation, registration and 3D tracking. The developed AR system is tested on mobile phones web-browsers providing clear insights on the performance of the system. Promising results highlight a number of frame per second varying between 27 and 60 for a real-time point budget of 4.3 million points. The point cloud tested is composed of 29 million points and shows how our indexation strategy permits the integration of massive point clouds aiming at the point budget. The results also gives research directions concerning the dependence and delay related to the quality of the network connection, and the battery consumption since portable sensors are used all the time.
OWL-T for a Semantic Description of IoT

This paper discusses the steps for developing a semantic description of things in the context of Internet-of-Things (IoT). This description is deemed necessary to discover things prior to confirming their participation in complex business scenarios. Existing approaches provide a restrictive view of how things should be semantically described overlooking the fact that things operate in a complex IoT ecosystem in which they have capabilities to offer and need resources to consume and peers to interact with. To address this restrictive view, Ontology Web Language for Things (OWL-T) is put forward in this paper. OWL-T revolves around 3 dimensions known as interaction, operation, and consumption. Each dimension encompasses abstract conceptual areas that are instantiated using concrete areas, which allows to produce a dedicated IoT ontology.
Barrier Lyapunov function–based adaptive neural network control for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints via command filtering

This article considers the problem of adaptive neural network control via command filtering for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints and parameter uncertainties. First, a neural network state observer based on a K-filter is established to reconstruct unmeasured feedback information. Then, the command filtered technology is used to overcome the inherent “explosion of complexity” problem under fractional-order framework. Furthermore, to eliminate the errors generated by filters, an error compensation system is used. Meanwhile, the nonlinear unknown functions are approximated by using neural networks. In addition, the barrier Lyapunov functions are designed to avoid the violation of the state constraints. Finally, the availability of the proposed control algorithm is revealed by numerical simulations.
Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction

Performing efficient inference on high dimensional discrete Bayesian Networks (BNs) is challenging. When using exact inference methods the space complexity can grow exponentially with the tree-width, thus making computation intractable. This paper presents a general purpose approximate inference algorithm, based on a new region belief approximation method, called Triplet Region Construction (TRC). TRC reduces the cluster space complexity for factorized models from worst-case exponential to polynomial by performing graph factorization and producing clusters of limited size. Unlike previous generations of region-based algorithms, TRC is guaranteed to converge and effectively addresses the region choice problem that bedevils other region-based algorithms used for BN inference. Our experiments demonstrate that it also achieves significantly more accurate results than competing algorithms.
Validation of the Smart City as a Sustainable Development Knowledge Tool: The Challenge of Using Technologies in Education during COVID-19

The objective of this research was to design and validate a questionnaire for teachers on the knowledge and use of smart city concepts and their usefulness in online learning during the COVID-19 pandemic based on a sustainable approach at different educational levels. It is important to analyze the use of online education based on the construction of intelligent learning environments, which favor the interaction between the environment and the student. With a quasi-experimental methodology, the psychometric properties of the questionnaire were analyzed through structural equation modeling (SEM), and the model was adjusted through a multivariate regression analysis to relate response patterns to a set of latent factors that cannot be directly observed, but exist in continuous dimensions of the people evaluated, and to create a valid and reliable instrument as a measurement tool using a sample of n = 973 subjects. The sample distribution consisted of 22.36% primary school teachers, 59.01% high school teachers, and 18.56% university teachers. The results showed a high reliability and construct validity through two models, and adjustment of the original model produced better goodness-of-fit parameters. We conclude that the designed questionnaire is a useful and valid tool for understanding how teachers have approached online teaching during the pandemic and their knowledge and use of Smart City concepts.
Improving Reliability of Clinical Models Using Prediction Calibration

The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous introspection of model behavior. In supervised and semi-supervised learning, prediction calibration has emerged as a key technique to achieve improved generalization and to promote trust in learned models. In this paper, we investigate the effectiveness of different prediction calibration techniques in improving the reliability of clinical models. First, we introduce reliability plots, which measures the trade-off between model autonomy and generalization, to quantify model reliability. Second, we propose to utilize an interval calibration objective in lieu of the standard cross entropy loss to build classification models. Finally, using a lesion classification problem with dermoscopy images, we evaluate the proposed prediction calibration approach against both uncalibrated models as well as existing prediction calibration techniques such as mixup and single-shot calibration.
An Assistive Magnetic Skin System: Enabling Technology for Quadriplegics

People with quadriplegia no longer have control over their legs, neither the hands and cannot continue living their life independently. On top of that, severely injured quadriplegics (i.e., C1 and C2 injuries) suffer from speaking difficulties and minimal head and neck movements. With the advancement in wearable artificial skins and the Internet‐of‐Things, realizing comfortable and practical solutions for quadriplegics is more tangible than ever. Herein, a comprehensive assistive magnetic skin system is presented that allows quadriplegics, including the severely injured ones, to move around individually and control their surroundings with ease. The system tracks facial expressions by tracking the movement of magnetic tattoos attached to the face, using magnetic field sensors incorporated into eyeglasses. The magnetic tattoos are made of highly flexible, stretchable, breathable, and biocompatible magnetic skins. In combination with smart‐glasses, smart‐wheelchair, and smart‐gadgets, the users can move around and control their environment with their facial expressions. The system is also designed to allow quadriplegics to perform outdoor activities effortlessly. It supports line‐of‐sight communication and does not require pretethering to the smart‐gadgets, unlike the existing solutions. Thus, enabling the user to walk on pathways, activate pedestrian lights, control public elevators, and perform various outdoor activities independently.
An Empirical Study of Generation Order for Machine Translation

In this work, we present an empirical study of generation order for machine translation.Building on recent advances in insertion-based modeling, we first introduce a soft orderreward framework that enables us to train models to follow arbitrary oracle generation policies.We then make use of this framework to explore a large variety of generation orders, including uninformed orders, locationbased orders, frequency-based orders, contentbased orders, and model-based orders.Curiously, we find that for the WMT'14 English → German and WMT'18 English → Chinese translation tasks, order does not have a substantial impact on output quality.Moreover, for English → German, we even discover that unintuitive orderings such as alphabetical and shortest-first can match the performance of a standard Transformer, suggesting that traditional left-to-right generation may not be necessary to achieve high performance.
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots

Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents. As a result, deep neural models such as sequence-to-sequence, memory networks, and the Transformer have become key ingredients of state-of-the-art dialog systems. While those models are able to generate meaningful responses even in unseen situations, they need a lot of training data to build a reliable model. Thus, most real-world systems have used traditional approaches based on information retrieval (IR) and even hand-crafted rules, due to their robustness and effectiveness, especially for narrow-focused conversations. Here, we present a method that adapts a deep neural architecture from the domain of machine reading comprehension to re-rank the suggested answers from different models using the question as a context. We train our model using negative sampling based on question–answer pairs from the Twitter Customer Support Dataset. The experimental results show that our re-ranking framework can improve the performance in terms of word overlap and semantics both for individual models as well as for model combinations.
Dissecting Span Identification Tasks with Performance Prediction

Span identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text.Despite being a staple of NLP, and sharing a common structure, there is little insight on how these tasks' properties influence their difficulty, and thus little guidance on what model families work well on span ID tasks, and why.We analyze span ID tasks via performance prediction, estimating how well neural architectures do on different tasks.Our contributions are: (a) we identify key properties of span ID tasks that can inform performance prediction; (b) we carry out a large-scale experiment on English data, building a model to predict performance for unseen span ID tasks that can support architecture choices; (c), we investigate the parameters of the meta model, yielding new insights on how model and task properties interact to affect span ID performance.We find, e.g., that span frequency is especially important for LSTMs, and that CRFs help when spans are infrequent and boundaries non-distinctive.
Exploring Contextualized Neural Language Models for Temporal Dependency Parsing

Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and timerelated question answering.It is a challenging problem which requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep contextualized language models (LMs) such as BERT (Devlin et al., 2019).In this paper, we develop several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue, 2018a).We also present a detailed analysis on why deep contextualized neural LMs help and where they may fall short.Source code and resources are made available at https://github.com/bnmin/tdp_ranking.
Perkembangan Gamification dan Dampak Game Online terhadap Jiwa Manusia di Kota Pintar DKI Jakarta

In this study, online games are a research problem that is raised, by raising all things in online games, the author wants to know the impact of online games that have been troubling society and the results of this study are suggestions that will make the basic ingredients of decision making in terms of online games has troubled all levels of society. This research method uses literature review and field review, with the two process methods used it will produce valid research, with research based on previous research, the research will be more renewable, and research carried out in the field will find out the problems in the field. There have been many studies discussing the effect of online games, and in this study discussing the impact on the human psyche. In this study, researchers took data from many parties who were deemed necessary, and made the basis of their research. This research will produce suggestions and proposals that will be applied in a smart city in making a policy in an online application that is troubling the public.
WantWords: An Open-source Online Reverse Dictionary System

A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions.Reverse dictionaries have great practical value such as solving the tip-of-the-tongue problem and helping new language learners.There have been some online reverse dictionary systems, but they support English reverse dictionary queries only and their performance is far from perfect.In this paper, we present a new open-source online reverse dictionary system named WantWords (https://wantwords.thunlp.org/).It not only significantly outperforms other reverse dictionary systems on English reverse dictionary performance, but also supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries for the first time.Moreover, it has user-friendly front-end design which can help users find the words they need quickly and easily.
Guided Dialogue Policy Learning without Adversarial Learning in the Loop

Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.However, these methods suffer from sparse and unstable reward signals returned by a user simulator only when a dialogue finishes.Besides, the reward signal is manually designed by human experts, which requires domain knowledge.Recently, a number of adversarial learning methods have been proposed to learn the reward function together with the dialogue policy.However, to alternatively update the dialogue policy and the reward model on the fly, we are limited to policy-gradient-based algorithms, such as REINFORCE and PPO.Moreover, the alternating training of a dialogue agent and the reward model can easily get stuck in local optima or result in mode collapse.To overcome the listed issues, we propose to decompose the adversarial training into two steps.First, we train the discriminator with an auxiliary dialogue generator and then incorporate a derived reward model into a common RL method to guide the dialogue policy learning.This approach is applicable to both on-policy and off-policy RL methods.Based on our extensive experimentation, we can conclude the proposed method:(1) achieves a remarkable task success rate using both on-policy and off-policy RL methods; and (2) has potential to transfer knowledge from existing domains to a new domain.
Virtual Reality and Augmented reality applications in agriculture: a literature review

Virtual Reality (VR) and Augmented Reality (AR) based technologies can be applied in several areas, such as: science, services, education, medicine and military. On the other hand, agriculture is an area that has benefited from computer systems, with technological advances that have culminated in fields of study such as Agriculture 4.0, Digital Agriculture and Precision Agriculture, providing an increase in food production in a sustainable way. Thus, the agricultural area can be a target for professionals and researchers in VR and AR. In this article a literature review is presented with the objective of showing why VR and AR technologies are beneficial to the agricultural sector. The searches were conducted on Google Scholar, ACM Digital Library, Elsevier, IEEE Xplore, MDPI and Springer. After searching and selecting, twenty articles were analyzed. The review describes the main input and output devices used, the main crops, the objectives of the applications, the users, the countries where the studies were conducted. The results show that digital agriculture is able to help users to increase productivity in a sustainable way. The review identified trends, challenges and opportunities for improving these technologies applied to agriculture.
Higher education innovatics as the newest interdisciplinary direction of higher school and higher education science

For real European integration of Ukraine and increasing the scienti c level of the Ukrainian higher education, the introduction of innovative methods and techniques is necessary. For a correct understanding of the innovative directions of reforming higher education, it is necessary to create a scienti c apparatus that combines the theory and practice of innovative development of higher education and the entire system of higher education. Authors propose to introduce a new scienti c direction in the sphere of higher education with a new scienti c term | \Innovatics of higher education" or \higher education innovatics". The theoretical foundations and practical provisions of the new scienti c direction in the sphere of higher education | the innovatics in higher education includes a complex of innovative changes in such inherent in higher education types of activities as: teaching, training, study; scienti c and R &amp; D activities, new technic and technologies development, construction and design creativity; cultural and moral development, upbringing of human values; education of honesty and justice, patriotism and peacefulness; instilling love and tolerance for one's neighbor, mercy and charity, compassion; engineering, technical, informational activities; nancial and economic support of the educational process and R &amp; D, operating and business expenses, development and expansion of activities; inventive and patent-licensing activities, technology transfer; academic or university entrepreneurship; sports, recreational, festive and extracurricular activities; and other types of activities of universities, colleges and other institutions related / involved / associated with higher education. Thus, innovatics in higher education (as the complex of innovations in the whole sphere of higher education) includes innovative changes in almost all areas of higher education to enhance and/or improve the quality training of professionally prepared and responsible citizens of the modern community and for development of university R &amp; D. These can be useful for reforming the national higher education system of Ukraine and for the development of a new scienti c direction in the eld of higher education.
Going Digital: Persistent Identifiers for Research Samples, Resources and Instruments

The uptake of Persistent Identifiers (PIDs) has increased in recent years and has improved the Findability, Accessibility, Interoperability and Reusability (FAIR) of various research related objects (e.g., data, software, researchers and research organisations). The uptake of PIDs for physical aspects of research (such as samples, artefacts, reagents and analyses instruments) has thus far been embraced primarily for use in the fields of Earth and life Sciences. Wider adoption of PIDs for physical aspects of research can improve the findability and accessibility of these resources, which will allow for data to be put into more detailed context. By using PIDs all the information about a sample or artefact could be more easily available in a single location, allowing for persistent links to other sources of relevant information. Through the use of interoperable (metadata) standards and shared forms of documentation it will be easier to collaborate across multiple disciplines and the reusability of resulting data and the physical samples and artefacts themselves will improve. Wider adoption of PIDs for physical aspects of research is challenging, as research communities will have to work together to establish relevant standards that are meaningful across multiple domains. The infrastructure for wider adoption already exists, it is now up to research communities to adopt standards and PIDs for the physical aspects of their research and up to funding and research institutes to support this broader adoption.
Remote Sensing Monitoring of Spatial–Temporal Variation of Soil Salinization before and after Irrigation in the Yellow River Delta

Sun, H.; Xu, L.; Wang, J., and Fu, X., 2020. Remote sensing monitoring of spatial–temporal variation of soil salinization before and after irrigation in the Yellow River Delta. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 56–60. Coconut Creek (Florida), ISSN 0749-0208.To study the impact of irrigation on soil salinization in the Yellow River Delta, changes in soil salinization before and after irrigation were analyzed using Gaofen-1 wide-field-view remote sensing images and the soil salinization remote sensing monitoring index. The results show the following: (1) The correlation coefficient between the salinity index and the measured soil salinity was above 0.6, which met the requirements for soil salinization monitoring. (2) During irrigation, the area of nonsaline soil was more than 97%, and the saline soil was transformed into nonsaline soil. (3) About 1 week after irrigation, the area of nonsaline soil was reduced by 11%, and the nonsaline soil was transformed into light salinized soil and moderate salinized soil. This study provides an accurate, efficient, and widely applicable method for the study of soil salinization before and after irrigation.