Reviewerly/exHarmony huggingface dataset:
Automatic first arrival picking combining transfer learning and the short-time Fourier transform

PreviousNext No AccessSEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019Automatic first arrival picking combining transfer learning and the short-time Fourier transformAuthors: Xiaofang LiaoJunxing CaoXudong JiangPeng WangShuyu WangXiaofang LiaoChengdu University of TechnologySearch for more papers by this author, Junxing CaoChengdu University of TechnologySearch for more papers by this author, Xudong JiangChengdu University of TechnologySearch for more papers by this author, Peng WangChengdu University of TechnologySearch for more papers by this author, and Shuyu WangChengdu University of TechnologySearch for more papers by this authorhttps://doi.org/10.1190/iwmg2019_05.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract First arrival picking is a key step in seismic data processing. In this study, we exploit the transfer learning properties of convolutional neural networks (CNNs) for automatic first arrival picking. By use of results from the short-time Fourier transform (STFT) as an input, two pre-trained CNNs, Alexnet and GoogLeNet are employed to identify and classify three types of seismic waves: noise, first-break and post-first-break. The first arrival times correspond to the peaks of classification probability of first-break. The proposed method is compared against adaptive multiband picking algorithm (AMPA) and short- and long-time average ratio (STA/LTA) using real data. These results indicated that the proposed method had high accuracy and robustness. Keywords: air gun, Fourier, neural networks, seismic attributes, algorithmPermalink: https://doi.org/10.1190/iwmg2019_05.1FiguresReferencesRelatedDetailsCited byBuilding training data set for deep learning-based P- and S-wave separation: Field data caseYanwen Wei, Yunyue Elita Li, and Haohuan Fu1 September 2021Automated first break picking with constrained pooling networksDavid Cova, Peigen Xie, and Phuong-Thu Trinh30 September 2020 SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019ISSN (online):2159-6832Copyright: 2020 Pages: 138 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 17 Jan 2020 CITATION INFORMATION Xiaofang Liao, Junxing Cao, Xudong Jiang, Peng Wang, and Shuyu Wang, (2020), "Automatic first arrival picking combining transfer learning and the short-time Fourier transform," SEG Global Meeting Abstracts : 17-21. https://doi.org/10.1190/iwmg2019_05.1 Plain-Language Summary Keywordsair gunFourierneural networksseismic attributesalgorithmPDF DownloadLoading ...
Design of Paper Book Oriented Augmented Reality Collaborative Annotation System for Science Education

The authors designed a kind of augmented reality annotation system based on network knowledge collaboration for primary science education to expand the cognitive effect. Various types of annotations such as text, images, videos, links, 3D models, etc. can be added to the corresponding position of the paper book by multi-user and multi-device through the system. And the annotation contents could be retrieved in AR mode by other users. The connotation and dimension of scientific knowledge could be expanded through concentrating diversified annotations. The system only records the relative position of the annotation on the page by a hand-aided registration, and uploads the location information to the server without infringing the copyright of the book. The system allows users to add links as annotations, through which users could interact with social media and knowledge communities. By using this system, users' ideas could be connected thus promote the flow of knowledge between different types of readers (such as students, parents, and teachers), readers and authors and it is conducive to the exchange and inspiration of ideas, promoting the integration of knowledge and the generation of group wisdom.
Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings

The Word2Vec model is a neural network-based unsupervised word embedding technique widely used in applications such as natural language processing, bioinformatics and graph mining. As Word2Vec repeatedly performs Stochastic Gradient Descent (SGD) to minimize the objective function, it is very compute-intensive. However, existing methods for parallelizing Word2Vec are not optimized enough for data locality to achieve high performance. In this paper, we develop a parallel data-locality-enhanced Word2Vec algorithm based on Skip-gram with a novel negative sampling method that decouples loss calculation with positive and negative samples; this allows us to efficiently reformulate matrix-matrix operations for the negative samples over the sentence. Experimental results demonstrate our parallel implementations on multi-core CPUs and GPUs achieve significant performance improvement over the existing state-of-the-art parallel Word2Vec implementations while maintaining evaluation quality. We also show the utility of our Word2Vec implementation within the Node2Vec algorithm which accelerates embedding learning for large graphs.
BID: An Effective Blind Image Deblurring Scheme to Estimate the Blur Kernel for Various Scenarios

In recent years, image deblurring has been widely investigated.In order to solve this ill-posed problem, a variety of prior models have been proposed successively.The two-tone prior has been successfully applied to text images deblurring and achieved significant results.However, the natural image with rich color clusters does not meet the two-tone prior, which requires only two color clusters in the image.In this paper, a local two-tone prior is proposed for images with complex color clusters, which decomposes the image with complex color clusters into patches with simple color clusters.We also find that the process of image blurring is a weighted average of pixel values, which will lead to an increase of intermediate pixel values.Therefore, a new measure of the dynamic range of the image is proposed, which indicates the difference of the color cluster using the average value of the color cluster, and it is rigorously proved in mathematics.Besides, we analyze the effectiveness of the proposed prior in image deblurring in detail.Experimental results on the widely used datasets show that the proposed method performs favorably against the state-of-the-art algorithms, both qualitatively and quantitatively.
AN ARTIFACT REDUCTION METHOD FOR BLOCK-BASED VIDEO CODING

Most of the image/video coding standards are based on discrete cosine transform which is a block-based coding scheme with a disadvantage of blocking effects at low bit rates. In this paper, we propose a hybrid method which consists of downsampling/upsampling, significance map coding and local cosine transform to reduce the disadvantage of lossy compression and is also compatible with current standards. Experimental results show that performance of the proposed method is better than the conventional approaches.
ROS-based robot offline planning simulation system

Abstract In order to realize the rapid and automatic generation of robot motion trajectory, a robot offline planning simulation system was developed based on ROS platform. This paper analyzes the model STL file, reconstructs the topology data structure, generates the model minimum bounding box, and obtains the model surface path through the slicing algorithm to generate the processing path. The system loads the Moveit motion planner, applies different motion planning methods, generates a collision-free, continuous motion trajectory, and visualizes the offline planning process. The simulation verification system can realize the functions of rapid path generation on the workpiece surface and obstacle avoidance motion planning of the robot arm. It can realize offline programming of robots and improve the efficiency of robot teaching.
Acquiring Strategies for the Board Game Geister by Regret Minimization

Counterfactual Regret Minimization (CFR) is proposed to be an effective method to find approximate Nash Equilibrium for large zero-sum imperfect information games. Deep neural networks make it possible for CFR to be applied in large games by extending tabular CFR to Deep CFR. While there is little research on applying CFR to board games, in this paper, we propose a variant of Deep CFR for board games and apply it to the game Geister. We show that our proposed method can train agents with an appropriate strategy.
Sequence Labeling Using Deep Neural Nets

Now a day's sequence labeling has become most interesting topic in the current technical era.Sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of sequence of observed values, and also, it is treated as an independent task.By using traditional methods such as HMM and CRF we can implement sequence labeling.Both the methods take the sequence of input and learn to predict an optimal sequence of labels.These are very powerful methods, but they have not experienced the great success due to some drawbacks like lack of semantic awareness and can't handle longer sequential dependencies.So by using deep learning techniques such as recurrent neural networks, they can capture the local dependencies and find longer patterns.Real world applications where the sequence labeling can be applied are Google search Engine.Where in the search box if we type some words automatically Google will suggest some sentences or words which makes our work easier.
Automatic Speech Intelligibility Detection for Speakers with Speech Impairments: The Identification of Significant Speech Features

Selection of relevant features is important for discriminating speech in detection based ASR system, thus contributing to the improved performance of the detector.In the context of speech impairments, speech errors can be discriminated from regular speech by adopting the appropriate discriminative speech features with high discriminative ability between the impaired and the control group.However, identification of suitable discriminative speech features for error detection in impaired speech was not well investigated in the literature.Characteristics of impaired speech are grossly different from regular speech, thus making the existing speech features to be less effective in recognizing the impaired speech.To overcome this gap, the speech features of impaired speech based on the prosody, pronunciation and voice quality are analyzed for identifying the significant speech features which are related to the intelligibility deficits.In this research, we investigate the relations of speech impairments due to cerebral palsy, and hearing impairment with the prosody, pronunciation, and voice quality.Later, we identify the relationship of the speech features with the speech intelligibility classification and the significant speech features in improving the discriminative ability of an automatic speech intelligibility detection system.The findings showed that prosody, pronunciation and voice quality features are statistically significant speech features for improving the detection ability of impaired speeches.Voice quality is identified as the best speech features with more discriminative power in detecting speech intelligibility of impaired speech.
A CAD System for Liver Cancer Diagnosis Based on Multi-Phase CT Images Features with BP Network

Because of the difficulty of CT imaging diagnosis of hepatocellular carcinoma, it is necessary to research the system of Computer-aided Diagnosis (CAD). Most practical diagnostic schemes use single image features to input single-phase images for classification. In this paper, a diagnosis system is designed based on BP neural network after dimensionality reduction by PCA, which combines multiple features and multi-phase information. The experimental results show that the comprehensive accuracy of this system reaches 96.98%, which is superior to the single-phase CAD system and comparable with conventional CNN.
Human-Generated and Machine-Generated Ratings of Password Strength: What Do Users Trust More?

Proactive password checkers have been widely used to persuade users to select stronger passwords by providing machine-generated strength ratings of passwords.If such ratings do not match human-generated ratings of human users, there can be a loss of trust in PPCs.In order to study the effectiveness of PPCs, it would be useful to investigate how human users perceive such machine-and human-generated ratings in terms of their trust, which has been rarely studied in the literature.To fill this gap, we report a large-scale crowdsourcing study with over 1,000 workers.The participants were asked to choose which of the two ratings they trusted more.The passwords were selected based on a survey of over 100 human password experts.The results revealed that participants exhibited four distinct behavioral patterns when the passwords were hidden, and many changed their behaviors significantly after the passwords were disclosed, suggesting their reported trust was influenced by their own judgments.
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<p style='text-indent:20px;'>Subfield subcodes of algebraic-geometric codes are good candidates for the use in post-quantum cryptosystems, provided their true parameters such as dimension and minimum distance can be determined. In this paper we present new values of the true dimension of subfield subcodes of <inline-formula><tex-math id="M1">\begin{document}$ 1 $\end{document}</tex-math></inline-formula>–point Hermitian codes, including the case when the subfield is not binary.
Implementation of Real-Time Skin Segmentation Based on K-Means Clustering Method

Localization and detection of body parts (in this case, hands) is an exigent issue in image processing, since it is a prerequisite for applications like hand gesture recognition. Through this paper, a proposal for implementing an efficient way for segmenting the skin tone is developed by applying appropriate Computer Vision techniques and K-means Clustering on each frame captured by the camera under different illumination conditions as well as in the complex backgrounds, is developed. In this method, detection of skin portions using color identification, and segmentation from the image is done for its implementation in real-time systems. While the paper focuses on using skin segmentation for the detection of hand movements, the application of this approach can easily be implemented in various applications involving Human Computer Interaction (HCI). Examples of such include mouse cursor movement, media player application, writing text on electrical documents, controlling robot, detects the pointing location, sign language, hand posture and face recognition.
A Virtual-Reality Edu-Game: Save The Environment from the Dangers of Pollution

The virtual reality educational game, with theme “saving the environment from the danger of pollution” – aims to help children aged 10-13 years in understanding pollution and how to reduce it. The game uses smartphone and VR Box. This edu game is designed under SDLC (Software Development Life Cycle) of the waterfall model. Contained assets are obtained from the Unity Asset Store which will simulate learning techniques with several educational games. This can be seen as an advantage from the traditional learning method. Based on a black box test, this game runs well on target devices. Usability test is conducted using the System Usability Scale (SUS), and this game gets a score of 71.58 which is in the "good" criteria.
Early Prediction of Sepsis From Clinical Data via Heterogeneous Event Aggregation

Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventive treatment. However, the early prediction is challenging because patients’ sequential data in EHR contains temporal interactions of multiple clinical events. And capturing temporal interactions in the long event sequence is hard for traditional LSTM. Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM. Our proposed Heterogeneous Event Aggregation can not only shorten the length of clinical event sequence but also help to retain temporal interactions of both categorical and numerical features of clinical events in the multiple heads of the aggregation representations. In the PhysioNet/Computing in Cardiology Challenge 2019 [1], with the team named PKU_DLIB, our proposed model, in high efficiency, achieved utility score (0.321) in the full test set.
Examining Factors Affecting Student Engagement in English Preparatory Classes According to Student Opinions

This study aimed to determine the classroom engagement levels of university students studying English Preparatory Classes, and the factors affecting their engagement in the classes.In the study, survey method, which is one of the qualitative research methods, was employed.The population of the study consisted of students selected from Bilecik Seyh Edebali University's "Optional English Preparatory Classes" during 2016-2017 academic year.The sample of the study consisted of 67 students selected from the population through random sampling.Mixed method was used to obtain the data, with quantitative data obtained from 67 students, and qualitative data obtained from nine students determined by criterion sampling method.In the study, the "Classroom Engagement Scale for University Students" developed by Eryılmaz (2014), and the "Learning Climate Questionnaire" adapted to Turkish language by Kanadlı and Bağçeci (2016) were both employed.Qualitative data were collected through open-ended questions prepared by the researchers, and content analysis was performed in its analysis.As a result of the study, it was determined that the emotional and cognitive engagement levels of the students were at an appropriate level, and that behavioral engagement was at a slightly appropriate level.The students stated that personal factors, characteristics of the program, implementation of the program and factors related to teacher behavior affected their engagement in English language preparatory classes.
Midpoint Distance Circle Generation Algorithm based on Midpoint Circle Algorithm and Bresenham Circle Algorithm

Abstract In the process of generating a circle, the existing midpoint circle algorithm and Bresenham circle algorithm have the problems of slow drawing speed, high resource consumption, complicated calculation steps and large pixel spacing in some areas, and a combination of midpoint painting is proposed. A new algorithm for the circular algorithm and the Bresenham circular algorithm. The algorithm first calculates the positional relationship between the midpoint and the circumference of two adjacent pixel points, and then calculates the distance difference between two adjacent pixel points and the circle by two approximate distances, and then obtains the decision parameters of the algorithm. A complete circle is obtained by the symmetry of the octant. Experiments show that the proposed algorithm is an effective midpoint distance circle generation algorithm based on the midpoint circle algorithm and the Bresenham algorithm. Compared with similar algorithms, it has higher stability and precision, and has less resource consumption and more flexible calculation methods.
Cloud Infrastructure for Creation of Interpretable Diagnostic Knowledge Bases of Diseases Regardless their Etiology

The paper presents a cloud infrastructure for creation of interpretable diagnostic knowledge bases for intelligent decision support systems in medicine.An ontology of medical diagnostics allows us to form diseases regardless their etiology as a multivariate developing internal process.The relations types being defined in the ontology allow users to create a knowledge base sufficient for search of hypotheses of possible single diagnoses, for detection of combined and complicated pathologies, and for reduction of a set of hypotheses of diagnosis.The offered infrastructure includes the following tools: the knowledge base editor being generated on ontology, the terminology database editor, the tool for estimation of knowledge base quality and some instruments of automation of development and improvement of knowledge base.The infrastructure is implemented on the IACPaaS platform and used by experts to create knowledge bases in various fields of medicine.The article is useful for developers of medical diagnostics systems.
Practices, Challenges, and Prospects of Big Data Curation: a Case Study in Geoscience

Open and persistent access to past, present, and future scientific data is fundamental for transparent and reproducible data-driven research. The scientific community is now facing both challenges and opportunities caused by the growingly complex disciplinary data systems. Concerted efforts from domain experts, information professionals, and Internet technology experts are essential to ensure the accessibility and interoperability of the big data. Here we review current practices in building and managing big data within the context of large data infrastructure, using geoscience cyberinfrastructure such as Interdisciplinary Earth Data Alliance (IEDA) and EarthCube as a case study. Geoscience is a data-rich discipline with a rapid expansion of sophisticated and diverse digital data sets. Having started to embrace the digital age, the community have applied big data and data mining tools into the new type of research. We also identified current challenges, key elements, and prospects to construct a more robust and future-proof big data infrastructure for research and publication for the future, as well as the roles, qualifications, and opportunities for librarians/information professionals in the data era.