Reviewerly/exHarmony huggingface dataset:
Automatic detection of first arrival time of seismic waves with the fine-tuning: Applying to observed data in Hachijojima

PreviousNext No AccessProceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021Automatic detection of first arrival time of seismic waves with the fine-tuning: Applying to observed data in HachijojimaAuthors: Hikaru KunimasaHiroyuki AzumaYoshiya OdaHikaru KunimasaTokyo Metropolitan UniversitySearch for more papers by this author, Hiroyuki AzumaTokyo Metropolitan UniversitySearch for more papers by this author, and Yoshiya OdaSearch for more papers by this authorhttps://doi.org/10.1190/segj2021-032.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Hachijojima is classified as an active volcano, and a dike intrusion was reported in 2002 (Kimata et al., 2004). To evaluate the eruption risk of the volcano, we will analyze underground structures by techniques using natural earthquakes. Hence, we need many and accurate first arrival of seismic waves generated by natural earthquakes to improve the analysis accuracy. However, now, seismic activity is low around Hachijojima. Therefore, the purpose of our study is an increase in the number of earthquakes available for analysis by detecting earthquakes with the deep-learning technique. The detection using the deep-learning can reduce labor costs and may increase the number of earthquakes compared to human detection. The recent development of the deep-learning is leading innovations in various fields. But deep-learning requires a large-scale dataset. It takes huge time and effort to prepare a large-scale dataset. To solve this problem, we use a method called the fine-tuning. The fine-tuning is a technique that re-trains parts of a trained model with small amounts of data and enables to extract the features of data efficiently. We applied Generalized Seismic Phase Detection with Deep learning (GPD) (Ross et al., 2018) to waveform data observed in Hachijojima. But there were many false detections. Therefore, we applied the fine-tuning to solve the problem. Keywords: deep-learning, the fine-tuning, phase, automatic detection, first arrival of seismic wavesPermalink: https://doi.org/10.1190/segj2021-032.1FiguresReferencesRelatedDetails Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 349 publication data© 2021 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Nov 2021 CITATION INFORMATION Hikaru Kunimasa, Hiroyuki Azuma, and Yoshiya Oda, (2021), "Automatic detection of first arrival time of seismic waves with the fine-tuning: Applying to observed data in Hachijojima," SEG Global Meeting Abstracts : 116-119. https://doi.org/10.1190/segj2021-032.1 Plain-Language Summary Keywordsdeep-learningthe fine-tuningphaseautomatic detectionfirst arrival of seismic wavesPDF DownloadLoading ...
Summary of Natural Language Generated SQL Statements

Abstract The entry cost of database query SQL statement is high, which is difficult for most database users. Therefore, natural language automatic generation of SQL sentences has gradually become the leading research direction in recent years. Previous researchers have done much excellent work in this area. This article mainly summarizes generating SQL statements in common natural language at present and conducts a detailed analysis. We summarize the current problems faced by SQL statements’ natural language generation and look forward to future development trends.
Sentiment Analysis Covid-19 Vaccination on Twitter Social Media Using Naïve Bayes Method

The government regulations regarding the implementation of vaccinations to tackle the COVID-19 pandemic. The regulation was issued by the Minister of Health Number ten of 2021. This program raises pros and cons so that it requires feedback for evaluation. Feedback can be obtained from opinions and stories that users convey through social media such as Twitter. This study aims to develop a model to determine public sentiment towards Covid-19 vaccination in three topics, namely the vaccination program, the effect of vaccination and the Covid-19 vaccine. The classification method used in this research is Bernoulli Naïve Bayes and Logistic Regression. The results of the comparison of the two methods show that Bernoulli Naïve Bayes gets better accuracy results. The number of tweet messages processed from Twitter is 5877. The model was tested to read public sentiment on Twitter from 7 September to 21 September 2021. The model concluded that public opinion regarding the vaccination program and the effect of vaccination tended to be positive. And opinions regarding the Covid-19 vaccine topic tend to be neutral. For further research, it can be developed by adding datasets.
Multitarget Tracking Based on Dynamic Bayesian Network With Reparameterized Approximate Variational Inference

Multitarget tracking (MTT) is an important component of situation-awareness based on the Internet of Things (IoT). Existing algorithms mainly focus on tracking based on conventional measurements, e.g., bearings or ranges. However, measurement parameter estimations are considered in isolation, limiting the accuracy and resolution of MTT, and the related data association is an NP-hard multidimensional assignment problem. In this article, we develop a new one-step MTT algorithm based on a novel dynamic Bayesian network (DBN), i.e., DBNMTT. The new MTT algorithm directly infers target states from the raw measurement data by fusing the array signal model, the signal propagation model, and the motion model. In this new DBNMTT framework, we treat target states and conventional measurements, such as bearings and target energies as hidden random variables. The posterior joint probability optimization problem is translated into the problem of graphical model learning. In this way, we can improve the accuracy and resolution of MTT and convert the NP-hard data association problem to a hidden variable learning problem. For nonconjugate models in the DBNMTT, we develop a novel reparameterized approximation variational inference (ReAVI) approach to solve the learning problem. The ReAVI converts nonconjugate models to conjugate models with new parameters and reuses the mean-field algorithm. The performance of our proposed new MTT method, namely, DBNMTT based on ReAVI (DBNMTT-ReAVI), is analyzed on extensive simulations in challenging scenarios. The simulation results show that the DBNMTT-ReAVI algorithm is superior to conventional measurement-based MTT algorithms in several aspects, including the success probability, convergence, resolution, and accuracy.
Beyond technocentrism: Improving Lower-Achiever's English Listening Performance by Using Mobile-Assisted Language Learning on University of Technology students

The effectiveness of mobile-assisted learning allows educational effects to become more apparent, such as enhancing a more engaged, authentic, and interactive learning environment. Research already indicated that mobile learning provide positive effects on Second Language (L2) learning. However, the influence of Mobile Assisted Language Learning (M.A.L.L.) on lower-achieving college students’ listening performance is worth understanding. This experimental study aimed to investigate the effects of M.A.L.L. instruction on Freshman English student's listening achievement. The results showed that Taiwanese lower-achieving college students benefited significantly better from the M.A.L.L. instruction on listening outcomes.
Weakly Supervised Localization with Patch Detector for Fine-Grained Image Retrieval

The small intra-class variance and the large intra-class variance is the difficulty of fine-grained image retrieval. Therefore, extracting distinguishing features between images is particularly important. Two issues need to be resolved. On the one hand, extracting features at image-level rather than at object-level will be disturbed by background clutter. On the other hand, when feature extraction is performed on object-level images, only object-level features are not enough to distinguish different fine-grained images. In this paper, we propose a new system to overcome these problems. Firstly, attention object local module is used to predict the position of the object. Secondly, we utilize the idea of filters to capture class-specific discriminative patches. Experiments conducted on two widely used datasets show that our proposed method can achieve better performance.
Slot Sharing Mechanism in Multi-domain Dialogue Systems

Task-oriented dialogue systems are very important due to their wide range of applications. In particular, those tasks that involve multiple domains have gained increasing attention in recent years, as the actual tasks performed by virtual assistants often span multiple domains. In this work, we propose the Divide-and-Conquer Distributed Architecture with Slot Sharing Mechanism (DCDA-S2M) system, which includes not only a distributed system aimed at managing dialogues through the pipeline architecture, but also a slot sharing mechanism that allows the system to obtain information during a conversation in one domain and reuse it in another domain, in order to avoid redundant interactions and make the dialogue more efficient. Results show that the distributed architecture outperforms the centralized one by 21.51% and that the slot sharing mechanism improves the system performance both in the success rate and in the number of turns during the dialogue, demonstrating that it can prevent the agent from requesting redundant information.
Deep Clustering with Reinforcement Strategy

Deep clustering learns deep feature representations that solve clustering tasks with a deep autoencoder. However, blurred clustering is inevitable due to the lack of overall clustering environment information, which means that no significant differences were observed between clusters. To address this issue, we propose a memory enhanced model for deep clustering with reinforcement strategy, where a Memory Cell is introduced as a storage unit for surrounding imformation. Specifically, the whole process of the model is divided into three parts. Firstly, the original high-dimensional image data is mapped to latent feature space thorugh the pre-training process, and the latent feature representation is obtained and stored in Memory Cell. Secondly, the traditional K-means algorithm is applied to initialize the clustering center on the latent representation. Finally, the reward regression strategy in reinforcement learning based on the Bernoulli distribution is adopted to fine-tune the results. ACC, ARI and NMI as evaluation metrics, the proposed model shows its competitiveness on MNIST and Fashion-MNIST dataset against recent state-of-art models.
Application on small sample classification of ER rule classifier

Most classical classification methods require a large number of samples to be trained, so the classification accuracy will decline when the sample size is not large enough. Considering the parameters of the recently proposed Evidential Reasoning rule (ER rule) classifier can be set according to experience and expert knowledge, a small sample classification method based on the ER rule classifier is proposed in this study. The classification steps of ER rule classifier contain evidence acquisition, reliability calculation, aggregation of activated evidence, and optimization of the adjustable parameters. And the case study about sleep/wake classification is conducted, including experiments of classification by small sample and 10-fold cross-validation based on ER rule classifier, and classification by small sample based on six classical classification methods. Finally, the feasibility and effectiveness of the ER Rule classifier to classify small samples with high accuracy are verified by analyzing the results.
Analysis of skills required to get jobs in companies of electronics engineering domain

In recent years, Engineering students think that there are very few core electronic jobs available for engineering students. Core electronic jobs are hardware jobs that require skills like communication technology (wired and wireless), embedded system design and VLSI design. In this article, we have compiled approximately 400 core electronic job positions available in few of the world’s most well-known electronic companies like Apple, Intel, Tesla, AMD, IBM, etc. According to our analysis, all 350-400 core electronic jobs had a requirement of Bachelor's degree and 50 \% of them preferred to have a Master's degree. There were also 20 \% of core electronic job positions for Senior Engineers with the requirement of PhD. This manuscript is a brief review of the skills required to get those jobs. Core electronics companies are companies that specialize in specific electronics sectors and they are highly reputed for their expertise. Some of the job positions were common between various companies and similarly, some skills were required for multiple job positions. Those skills have been plotted using a bar graph based on their recurrence in the job positions. Recurrence of a skill is the number of positions that require that skill. Some of the important skills which were repeated in 50 \% of the companies were Python, VLSI, PCB Designing, CAD, etc. By reading this review paper, one can get an idea about the exact skills required to get a job in their dream electronics company.
Research on Fast Enhancement of Motion Blurred Image Based on Improved Deep Learning

In order to solve the problem that the current vision system produces motion blur during imaging, a fast enhancement method for motion blur images based on improved deep learning is proposed. By detecting the contour of the motion blur image target, the Wiener filtering restoration model is established, combined with the improved deep learning method to decompose the gray tone function, and the BNL-Means algorithm is used to calculate the similarity between high-frequency image blocks to improve the accuracy of spatial image feature extraction. Realize the enhancement of motion blurred images. Compared with the existing methods, it is proved by experiments that the accuracy of the fuzzy kernel estimation of the design method reaches 95.63%, which is higher than the comparison of the three literature methods. The method has a better effect of enhancing the motion blur image and has strong practicability.
A Cyber Physical System based Stochastic Process Language With NuSMV Model Checker

Nowadays, cyber physical systems are playing an important role in human life in which they provide features that make interactions between human and machine easier. To design and analysis such systems, the main problem is their complexity. In this paper, we propose a description language for cyber physical systems based on stochastic processes. The proposed language is called SPDL (Stochastic Description Process Language). For designing SPDL, two main parts are considered for Cyber Physical Systems (CSP): embedded systems and physical environment. Then these parts are defined as stochastic processes and CPS is defined as a tuple. Syntax and semantics of SPDL are stated based on the proposed definition. Also, the semantics are defined as by set theory. For implementation of SPDL, dependencies between words of a requirements are extracted as a tree data structure. Based on the dependencies, SPDL is used for describing the CPS. Also, a lexical analyzer and a parser based on a defined BNF grammar for SPDL is designed and implemented. Finally, SPDL of CPS is transformed to NuSMV which is a symbolic model checker. The Experimental results show that SPDL is capable of describing cyber physical systems by natural language.
Face and Head Detection for Back View Cases Using Gradient Features and Learning Architecture

Face detection is an important topic in computer vision. Although there were numerous face detection algorithms over the past decade, some challenging cases remain unresolved. In this paper, we focus on the images with back-view heads. First, features including the image gradient and chrominance are applied to selecting regions which may be hairs. Next, redundant bounding boxes are filtered out by a CNN-based regression model and non-maximum suppression. Finally, geometric information is used for further filtering. Simulations show that the proposed method improves the detection performance for back-view heads, which are hard to be addressed by state-of-the-art face detection methods.
Position control of an Omnidirectional Robot through Visual Servoing

The position control task of a mobile robot can be approached using different techniques. Among them, the Visual Servoing technique presents a control scheme using the data acquired through a camera, that in many cases simplifies the principal task, as generating a law control from image coordinates is more practical to implement. Considering these advantages, in this paper is presented a proposal of an ImageBased Visual Servoing control scheme to generate a trajectory between an initial position and a desired position that an omnidirectional robot would have to follow. The initial and desired position are identified as two images observed through a camera. Also, it is implemented a fuzzy logic system to manage gain values because of the possible changes in the system response speed. Finally, a simulation of the control scheme is presented, where it is showed that it is possible to reach the desired image, and it is concluded that the omnidirectional robot reaches the desired position. Also, it is shown figures related to the trajectory generated, the error functions, and the gain values during the process, as well as the error between the total distance desired and the distance obtained. Recommendations related to the kinematic model of the robot and the configuration of the fuzzy logic system are also presented.
Effect of regularity on learning in GANs

Generative Adversarial Networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the opposite (thus the “adversarial”) so as to come up with new, synthetic instances of data that can pass for real data. GANs have been highly successful on datasets like MNIST, SVHN, CelebA, etc but training a GAN on large scale datasets like ImageNet is a challenging problem because they are deemed as not very regular. In this paper, we perform empirical experiments using parameterized synthetic datasets to probe how regularity of a dataset affects learning in GANs. We emperically show that regular datasets are easier to model for GANs because of their stable training process.
The Method for Estimating the Maximum Velocity of the AUV Motion with Trajectories Re-Planning According to their Dynamic Properties

Abstract The paper considers the problem of adjusting the value of a predetermined velocity required for horizontal motion of autonomous underwater vehicles (AUVs) in an environment containing obstacles, when trajectories change in order to avoid obstacles. Therewith, the velocity estimation generated during the re-planning of the AUVs motion trajectories is the maximum possible and is carried out on the basis of the AUVs dynamics model, considering their dynamic limitations and changes in the parameters of the motion trajectories. The topicality of the task is determined by the need to improve the efficiency of underwater missions in various areas of human activity (environmental monitoring, laying and maintenance of underwater communications, etc.). It depends on the mode of the AUVs motion: their velocity and parameters of the trajectories. The simulation results confirm the efficiency of the proposed method for estimating the maximum possible velocity of the AUVs motion.
FEE: An Event Extraction Model for Flood and Drought Disaster Based on Sequence Labeling

Due to unique climatic characteristics, China suffers from frequent disasters such as typhoons, droughts, and floods almost every year. It is necessary to study the disasters from history. The event extraction model is effective to mine disaster events from historical documents to help people organize historical event information in a more structured way. Common approaches transform the event extraction task into a sequence labeling task by modeling words with a static vector regardless of polysemy, which affects the performance of event extraction. We propose an event extraction model named FEE. It introduces dynamic word vectors to the BiLSTM-CRF mechanism, which solves the problem that existing models do not consider the ambiguity of words, and makes the result of event extraction more accurate. Experimental results on flood and drought disaster dataset and general dataset show that our model outperforms traditional models on precision, recall, and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> .
Design of a Laser Projection System for Intelligent Learning Environments

In this paper, the design and implementation of a laser projection system for use in intelligent learning environments is presented. Such a system has the advantages of drawing interesting, bright and vivid laser animations, which draw the attention of students better than most standard display technologies. In addition, the projection may be done on actual physical items standing in front of the students, which opens up many possibilities for gamification and augmented reality use cases in education. Besides the hardware of the laser projection system and the associated firmware, we also designed, created and tested our own desktop software that optimizes and prepares the data for projection. Our goal is to enable the creation of high-quality laser animations while reducing the time and efforts needed by teachers to prepare them. Our experimental results show that our concept has good potential and that the hardware and firmware of the system perform very well. Our data optimization strategies implemented through the desktop software also gave excellent results. This means that intelligent learning environments may employ laser animations successfully and let students benefit from this new approach to presenting information.
Less is Less: When are Snippets Insufficient for Human vs Machine Relevance Estimation?

Traditional information retrieval (IR) ranking models process the full text of documents. Newer models based on Transformers, however, would incur a high computational cost when processing long texts, so typically use only snippets from the document instead. The model's input based on a document's URL, title, and snippet (UTS) is akin to the summaries that appear on a search engine results page (SERP) to help searchers decide which result to click. This raises questions about when such summaries are sufficient for relevance estimation by the ranking model or the human assessor, and whether humans and machines benefit from the document's full text in similar ways. To answer these questions, we study human and neural model based relevance assessments on 12k query-documents sampled from Bing's search logs. We compare changes in the relevance assessments when only the document summaries and when the full text is also exposed to assessors, studying a range of query and document properties, e.g., query type, snippet length. Our findings show that the full text is beneficial for humans and a BERT model for similar query and document types, e.g., tail, long queries. A closer look, however, reveals that humans and machines respond to the additional input in very different ways. Adding the full text can also hurt the ranker's performance, e.g., for navigational queries.
Perspectives on Directing a Graduate Cybersecurity Program

Building a graduate cybersecurity program involves bringing together various perspectives to include those of students, faculty, industry, administration, and academia. This article discusses integrating these factors based on knowledge, university requirements, research, and industry needs.