Reviewerly/exHarmony huggingface dataset:
A Deep Earthquake Catalog for Oklahoma and Southern Kansas Reveals Extensive Basement Fault Networks

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]A Deep Earthquake Catalog for Oklahoma and Southern Kansas Reveals Extensive Basement Fault NetworksAuthorsYongsooParkiDGregory C.BerozaiDWilliam L.EllsworthiDSee all authors Yongsoo ParkiDCorresponding Author• Submitting AuthorStanford UniversityiDhttps://orcid.org/0000-0001-5466-8004view email addressThe email was not providedcopy email addressGregory C. BerozaiDStanford UniversityiDhttps://orcid.org/0000-0002-8667-1838view email addressThe email was not providedcopy email addressWilliam L. EllsworthiDStanford UniversityiDhttps://orcid.org/0000-0001-8378-4979view email addressThe email was not providedcopy email address
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories

Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context.Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet).To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge.We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks 1 .Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task.Evaluation on WiC Task also indicates that our method can better capture word meanings in context.
Research on skydiving experience design under virtual reality technology

Explore the application strategy and development direction of virtual reality technology in skydiving experience design. Through the analysis of virtual reality technology, the basic concept, basic characteristics and basic composition are explored, the system design scheme based on virtual reality technology and LOD technology is proposed, and the overall structure of each part of the system, the composition and construction of hardware equipment, the function of each module of the software system and the important technology are demonstrated and analyzed. On the basis of this research, the feasibility of combining skydiving experience design with virtual reality technology is summarized. It is concluded that this design can break the limitation of space and time in skydiving experience, and avoid the danger brought by operation in real environment, so as to improve accuracy and economic benefits. Virtual reality technology is the product of the rapid development of computer technology, and the combination of skydiving experience design has realized the combination of science and art.
THE USE OF DISCORD APPLICATION IN VIRTUAL ENGLISH LEARNING: AN INVESTIGATION ON STUDENTS’ PERCEPTIONS

Discord application, a voice and text chat platform usually used for games, social, class, or business groups, started to be used as one of the alternative media in virtual learning. A deeper investigation was conducted to get more information about students’ perception of virtual learning by using Discord application. This study implemented a qualitative research design, especially a case study. The data were collected from tertiary education students who were experienced in virtual English learning by using Discord by distributing an online questionnaire and doing interviews. Based on the results of data analysis, it was found that 89.72% of the students positively perceived the use of Discord application in virtual English learning. Some advantages of virtual learning by using Discord application found in the study were elaborated.
Integrals of incomplete beta functions, with applications to order statistics, random walks and string enumeration

We study the probability that one beta-distributed random variable exceeds the maximum of two others, allowing all three to have general parameters. This amounts to studying Euler transforms of products of two incomplete beta functions. We provide a closed form for the general problem in terms of Kampé de Fériet functions and a variety of simpler closed forms in special cases. The results are applied to derive the moments of the maximum of two independent beta-distributed random variables and to find inner products of incomplete beta functions. Restricted to positive integer parameters, our results are applied to determine an expected exit time for a conditioned random walk and also to a combinatorial problem of enumerating strings comprised of three different letters, subject to constraints.
Minimax Adaptive Estimation for Finite Sets of Linear Systems

For linear time-invariant systems with uncertain parameters belonging to a finite set, we present a purely deterministic approach to multiple-model estimation and propose an algorithm based on the minimax criterion using constrained quadratic programming. The estimator tends to learn the dynamics of the system, and once the uncertain parameters have been sufficiently estimated, the estimator behaves like a standard Kalman filter.
Effect of Miner Incentive on the Confirmation Time of Bitcoin Transactions

Blockchain is a technology that provides a distributed ledger that stores previous records while maintaining consistency and security. Bitcoin is the first and largest decentralized electronic cryptographic system that uses blockchain technology. It faces a challenge in making all the nodes synchronize and have the same overall view with the cost of scalability and performance. In addition, with miners' financial interest playing a significant role in choosing transactions from the backlog, small fee or small fee per byte value transactions will exhibit more delays. To study the issues related to the system's performance, we developed an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M(t)/M^{N}/1$</tex> model. The backlog's arrival follows an inhomogeneous Poison process to the system that has infinite buffer capacity, and the service time is distributed exponentially, which removes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> transactions at time. Besides validating the model with measurement data, we have used the model to study the reward distribution when miners take transaction selection strategies like fee per byte, fee-based, and FIFO. The analysis shows that smaller fee transactions exhibit higher waiting times, even with increasing the block size. Moreover, the miner transaction selection strategy impacts the final gain.
Graph Feature Gating Networks

Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where the node representations are updated by aggregating and transforming the information from the neighborhood. Meanwhile, they adopt the same strategy in aggregating the information from different feature dimensions. However, suggested by social dimension theory and spectral embedding, there are potential benefits to treat the dimensions differently during the aggregation process. In this work, we investigate to enable heterogeneous contributions of feature dimensions in GNNs. In particular, we propose a general graph feature gating network (GFGN) based on the graph signal denoising problem and then correspondingly introduce three graph filters under GFGN to allow different levels of contributions from feature dimensions. Extensive experiments on various real-world datasets demonstrate the effectiveness and robustness of the proposed frameworks.
Grow Kisan: E-Agriculture App

Abstract: Web apps and services facilitate information, communication, entertainment, and leisure. Web apps have ushered in a new era. In India, information and communication technology (ICT) in agriculture is a relatively new subject aimed at increasing agricultural and rural output. It entails the development of novel ICT-based rural applications. Farmers can benefit from ICT innovation by receiving timely and accurate information and services, resulting in a more productive agricultural climate.This study offers a web-based tool for farmers that will help them with their agricultural tasks while also keeping them informed about new government programmes. “Grow Kisan” is a service that gives agriculture-related information to Indian farmers in rural areas who face financial and connectivity challenges. The concept makes use of existing government services and mobile services to deliver a solution to the current situation with the least amount of cost to the farmer. The application system provides customers with all of the information that is relevant for their own land, searches, on-line crop management, statistic profit summaries, a statistical summary of their land by cultivating a specific crop, and the FAO recommendation not to use water, but to use fertilisation to increase productivity; "Fertilization" With GPS and Web methods, the system allows effective land management, monitoring, and data supply. The device's interface is more "image-based" than "word based," whereby even analphabets will comprehend and utilise it. The initiative focuses largely on small landowners, who truly may do miracles if specific instructions are supplied for effective land management and knowledge on the crops most appropriate for their territory. Not only will the initiative offer the smallholder with a free callback service, but also some films and soundtracks to show how things are to be accomplished in the country. The most essential component is video conferencing since farmers may ask through video and demonstrate whether they have a problem with crops and soil. For example, how to apply a certain herbicide and how to seed the soil for improved production, etc. A mobile cloud service for responding to Tomato Diseases is a voice-based response technique (VBAT) that is designed for farmers to receive voice queries in their natural language utilising the keyboard for the use of mic or text inquiries. Via speech and text the user will get the appropriate responses. India's cellular subscribers are 900 million. Keywords: E-Agriculture, Crop information, E-commerce, weather prediction, Agro - assistant.
On a Variational and Convex Model of the Blake–Zisserman Type for Segmentation of Low-Contrast and Piecewise Smooth Images

This paper proposes a new variational model for segmentation of low-contrast and piecewise smooth images. The model is motivated by the two-stage image segmentation work of Cai-Chan-Zeng (2013) for the Mumford-Shah model. To deal with low-contrast images more effectively, especially in treating higher-order discontinuities, we follow the idea of the Blake-Zisserman model instead of the Mumford-Shah. Two practical ideas are introduced here: first, a convex relaxation idea is used to derive an implementable formulation, and second, a game reformulation is proposed to reduce the strong dependence of coupling parameters. The proposed model is then analysed for existence and further solved by an ADMM solver. Numerical experiments can show that the new model outperforms the current state-of-the-art models for some challenging and low-contrast images.
Norma

Unsupervised domain adaptation is the problem of transferring extracted knowledge from a labeled source domain to an unlabeled target domain. To achieve discriminative domain adaptation recent studies take advantage of target sample pseudo-labels to impose class-aware distribution alignment across the source and target domains. Still, they have some shortcomings such as making decisions based on inaccurate pseudo-labeled samples that mislead the adaptation process. In this paper, we propose a progressive deep feature alignment, called Norma, to tackle class-aware unsupervised domain adaptation for image classification by enforcing inter-class compactness and intra-class discrepancy through a hybrid learning process. To this end, Norma's optimization process is defined based on a novel triplet loss which not only addresses soft prototype alignment but also pushes away multiple negative centroids. Also, to extract maximum discriminative domain knowledge per iteration, we propose a joint positive and negative learning procedure along with an uncertainty-guided progressive pseudo-labeling on the basis of prototype-based clustering and conditional probability. Our experimental results on several benchmarks demonstrate that Norma outperforms the state-of-the-art methods.
Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN comprises the frustum sequence module, 3D instance segmentation module SNET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module ENET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved Convolutional Neural Network (CNN) based on image-driven point clouds.
Approximating the two-mode two-photon Rabi model

The Rabi model describes the simplest nontrivial interaction between a few-level system and a bosonic mode, featuring in multiple seemingly unrelated systems of importance to quantum science and technology. While exact expressions for the energies of this model and its few-mode extensions have been obtained, they involve roots of transcendental functions and are thus cumbersome and unintuitive. Utilizing the symmetric generalized rotating wave approximation (S-GRWA), we develop a family of approximations to the energies of the two-mode two-photon Rabi model. The simplest elements of the family are analytically tractable, providing good approximations in regimes of interest such as ultra- and deep-strong coupling. Higher-order approximate energies can be used if more accuracy is required.
Virtual Tours to Promote the Remote Customer Experience

Today, virtual reality and augmented reality can allow people to interact with products and places in a very realistic way. In this direction, the use of immersive virtual tours (VTs) can improve the users’ experience, their perceptions, attitudes and even intended behaviours as potential or actual consumers. The paper focuses on a traditional Italian cheese product and defines a transdisciplinary, multimodal approach where VT helps the remote customer experience based on a VT application to virtually visit a Parmigiano Reggiano cheese dairy, using cutting-edge virtual reality set-up. The paper describes how to create a virtual tour of industrial plants by mapping the main actions, from the storytelling definition, to the plant digitization, until the creation of the virtual, immersive and multimodal application using Unity3D. The VT combines visual experience with gesture recognition and audio stimulation, adding also olfactory cues, in order to create an interactive and realistic experience.
Facial Expression Recognition and Image Description Generation in Vietnamese

This paper discusses a facial expression recognition model and a description generation model to build descriptive sentences for images and facial expressions of people in images. Our study shows that YOLOv5 achieves better results than a traditional CNN for all emotions on the KDEF dataset. In particular, the accuracies of the CNN and YOLOv5 models for emotion recognition are 0.853 and 0.938, respectively. A model for generating descriptions for images based on a merged architecture is proposed using VGG16 with the descriptions encoded over an LSTM model. YOLOv5 is also used to recognize dominant colors of objects in the images and correct the color words in the descriptions generated if it is necessary. If the description contains words referring to a person, we recognize the emotion of the person in the image. Finally, we combine the results of all models to create sentences that describe the visual content and the human emotions in the images. Experimental results on the Flickr8k dataset in Vietnamese achieve BLEU-1, BLEU-2, BLEU-3, BLEU-4 scores of 0.628; 0.425; 0.280; and 0.174, respectively.
Gradient Monitored Reinforcement Learning

This article presents a novel neural network training approach for faster convergence and better generalization abilities in deep reinforcement learning (RL). Particularly, we focus on the enhancement of training and evaluation performance in RL algorithms by systematically reducing gradient's variance and, thereby, providing a more targeted learning process. The proposed method, which we term gradient monitoring (GM), is a method to steer the learning in the weight parameters of a neural network based on the dynamic development and feedback from the training process itself. We propose different variants of the GM method that we prove to increase the underlying performance of the model. One of the proposed variants, momentum with GM (M-WGM), allows for a continuous adjustment of the quantum of backpropagated gradients in the network based on certain learning parameters. We further enhance the method with the adaptive M-WGM (AM-WGM) method, which allows for automatic adjustment between focused learning of certain weights versus more dispersed learning depending on the feedback from the rewards collected. As a by-product, it also allows for automatic derivation of the required deep network sizes during training as the method automatically freezes trained weights. The method is applied to two discrete (real-world multirobot coordination problems and Atari games) and one continuous control task (MuJoCo) using advantage actor-critic (A2C) and proximal policy optimization (PPO), respectively. The results obtained particularly underline the applicability and performance improvements of the methods in terms of generalization capability.
Information extraction using a mixed method analysis of social media data: A case study of the police shooting during the anti-Sterlite protests at Thoothukudi, India

During unexpected social events, information extracted from social media content posted by the people could play a crucial role in understanding the public opinion about the event. In this study, a mixed method procedure, which combines automated and human-based methods, is proposed to mine information from tweets to understand people's thoughts toward an unexpected turn of events. The proposed framework was applied on tweets posted regarding the police shooting to disperse protesters during the anti-Sterlite protests on May 22, 2018, at Thoothukudi in the Indian state of Tamil Nadu. The tweets were analyzed in two ways: (i) sentiment classification with automated computational methods and (ii) qualitatively examining the context of the expressed sentiments. In the case of anti-Sterlite protests, people expressed mixed emotions toward the protests for the closure of the Sterlite plant. A large negative sentiment toward the police shooting could be gleaned from the tweets. Analyzing tweets by the proposed method provides clear insights regarding the incident, which in turn will aid in planning an emergency response.
Enhanced Intelligent Automated Method to MSRCR Algorithm for Image Enhancement

The images captured by a camera may need tweaking based on the lighting situations present at the scene captured. The extremities in the lighting and the position of the object may sometime degrade the image captured. To have a right image all the time this concept was developed. This paper focuses on the automated procedure which rides over the Multi Scale Retinex with Colour Restoration Algorithm for best possible results. This is a completely automated approach where the user need not do anything apart from giving the image as the input. The main problems in photography are overcome to a great extent using this procedure.
Personalized Information Retrieval from Friendship Strength of Social Media Comments

Social networks have become an important venue to express the feelings of their users on a large scale.People are intuitive to use social networks to express their feelings, discuss ideas, and invite folks to take suggestions.Every social media user has a circle of friends.The suggestions of these friends are considered important contributions.Users pay more attention to suggestions provided by their friends or close friends.However, as the content on the Internet increases day by day, user satisfaction decreases at the same rate due to unsatisfactory search results.In this regard, different recommender systems have been developed that recommend friends to add topics and many other things according to the seeker's interests.The existing system provides a solution for personalized retrieval, but its accuracy is still a problem.In this work, we have proposed a personalized query recommendation system that utilizes Friendship Strength (FS) to recommend queries.For FS calculation, we have used the Facebook dataset comprising of more than 22k records taken from four different accounts.We have developed a ranking algorithm that provides ranking based on FS.Compared with existing systems, the proposed system can provide encouraging results.Key research groups and organizations can use this system for personalized information retrieval.
Vandermonde sets, hyperovals and Niho bent functions

We consider relationships between Vandermonde sets and hyperovals. Hyperovals are Vandermonde sets, but, in general, Vandermonde sets are not hyperovals. We give necessary and sufficient conditions for a Vandermonde set to be a hyperoval. Therefore, we provide purely algebraic criteria for existence of hyperovals. Furthermore, we give necessary and sufficient conditions for the existence of hyperovals in terms of $g$-functions, which can be considered as an analog of Glynn's Theorem for o-polynomials.