Reviewerly/exHarmony huggingface dataset:
Language Documentation and Descriptive Linguistics

Chapter 3 describes the fundamental research questions, empirical approaches and findings of language documentation and descriptive linguistics. These are two closely interrelated linguistic subfields concentrating on the collection and/or analysis of primary data for the purposes of documenting and describing languages (corpora and grammars). Methodological issues include considerations on research objects, fieldwork, as well as techniques and procedures of data collection, editing, and analysis such as transcription, annotation, and elicitation. The chapter ends with recommendations for further reading and a list of short exercises and ideas for small research projects.
The importance of reduction noise of earthquake waveform to get the precision of “P and S arrival time”

PreviousNext No AccessProceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021The importance of reduction noise of earthquake waveform to get the precision of “P and S arrival time”Authors: Wiji RaharjoIndriati Retno PalupiNela ElisaArin KuncahyaniWiji RaharjoUniversitas Pembangunan Nasional Veteran Yogyakarta (Indonesia)Search for more papers by this author, Indriati Retno PalupiUniversitas Pembangunan Nasional Veteran Yogyakarta (Indonesia)Search for more papers by this author, Nela ElisaUniversitas Pembangunan Nasional Veteran Yogyakarta (Indonesia)Search for more papers by this author, and Arin KuncahyaniBMKG Stasiun Yogyakarta (Indonesia)Search for more papers by this authorhttps://doi.org/10.1190/segj2021-020.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Sometimes it is hard to decide in the P and S wave arrival time picking process of the earthquake waveform because of some factors. One of them is the noise factor. Noise can come from several parameters like human activity, seismograph mistaken of calibration, or other natural phenomena. Noise sometimes makes the difference between P and S waves is not clear like in volcanic earthquake waveform. Both of them are important in hypocentre location calculation. The mistake in the picking process of P and S can make the hypocentre location is incorrect or far from the earthquake source location. In this paper, some BMKG data of earthquakes located around Yogyakarta is used in this research. The noise is reduced using a low, band, or high pass filter. Then, the spectrogram process is applied to help in the picking process. Based on the spectrogram result, the earthquake waveform after noise reduction is more clear and it makes the user easier to decide about the arrival time of the P and S waves. The spectrogram can be processed in Python by a convolutional process between the waveform and the mother wavelet. When the spectrogram pattern is suitable with the waveform, the P and S wave arrival time determination will be easy. Keywords: noise reduction, earthquake waveform, P and S wave arrival time, PythonPermalink: https://doi.org/10.1190/segj2021-020.1FiguresReferencesRelatedDetails 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 Wiji Raharjo, Indriati Retno Palupi, Nela Elisa, and Arin Kuncahyani, (2021), "The importance of reduction noise of earthquake waveform to get the precision of “P and S arrival time”," SEG Global Meeting Abstracts : 71-74. https://doi.org/10.1190/segj2021-020.1 Plain-Language Summary Keywordsnoise reductionearthquake waveformP and S wave arrival timePythonPDF DownloadLoading ...
Study on the Mapping Relationship between Discharge Signal Parameters of External Insulation Equipment and Quantization Parameters of Ultraviolet Detection

In this paper, based on the experimental data, the nonlinear mapping relationship between the apparent in-discharge and the photon number of UV imager in the partial discharge of external insulation equipment is discussed in depth, and the mapping relationship is processed by decision tree judgment and neural network regression respectively. In the processing process, the pollution degree, gain, detection distance, humidity and photon number are taken as the optical signal quantization parameters, and the discharge quantity is taken as the electrical signal quantization parameters. The above two methods are used to process the mapping relationship of the data collected in the test. Through the comparative analysis of the processing results, it is found that the accuracy of decision tree can only reach 46.9%, while the accuracy of neural network regression analysis can reach 96.68%. Therefore, it is reasonable to use neural network to analyze the problem.
Web Based Composition using Machine Learning Approaches: A Literature Review

Web Service Composition technology involves the integration of various atomic web services to perform huge scale tasks. As the number of web services is continuously growing, the need for composite web services also increases simultaneously. Since 2000, academic and systematic research has significantly focused on Web-Based Composition (WBC) in many well-reputed scientific journals and presented insight on several issues related to semantic Web and machine learning. In this paper, we present a review on the main contributions related to WBC using Machine Learning approaches. Our analysis and evaluation of those papers give a remarkable insight into the different approaches and planning models with strategies that were developed by researchers and IT professionals. This literature review paper intends to give researchers, interested in WBC, a valuable source of information and also fast-track their researches towards future technologies related to artificial intelligence and machine learning in WBC. The related work onto WBC, its application, research gap, the current status of WBC, and future challenges are also discussed.
Feature Extraction Techniques for Shape-Based CBIR—A Survey

Content-based image retrieval or CBIR is a method used to retrieve images from large databases with the help of local features. These local features are mainly colour, shape and texture. These features can either be used individually or in conjunction with one another to perform CBIR. Here, several shape descriptors, benchmark as well as author developed techniques, used for shape-based CBIR have been discussed. The shape data is primarily obtained from the edge detected from images. The experimentations performed were mostly on benchmark data like COREL-1000, OLIVIA, COIL-100, PRODUCE-1400, etc. It has been observed that the methods developed by authors have outperformed the benchmark methods in terms of precision and recall.
Expert System to Predict Acute Inflammation of Urinary Bladder and Nephritis Using Naïve Bayes Method

Bad life habits such as not consuming enough water and often delaying the urge to urinate are the causes of bladder-related diseases. In this study, a solution is given to overcome this problem by developing an expert system that can predict acute inflammation of nephritis and urine bladder disease using one of the classification algorithm that is often used and gets a lot of attention from researchers in predicting problems, namely Naïve Bayes. Data utilized include a list of symptoms that include patient fever, nausea, lumbar discomfort, urinary pushing (continuous urination, urethra burning and micturition pain, itching and urethra fluid swelling). The results of the diagnostic analysis consist of a confusion matrix and system accuracy values. The accuracy value of predicting acute urine bladder disease is 83% and acute nephritis of renal pelvis origin is 96%.
An Analysis of Subject Competencies Applied in the Activity Tasks of the ‘Home Life and Safety’ area in Middle School Technology-Home Economics Textbooks based on the 2015 Revised National Curriculum

The purpose of this study was to analyze two subject competencies (practical problem-solving capability and independent life capability) reflected in the activity tasks included in the ‘home life and safety’ area of 12 middle school technology-home economics textbooks in accordance with the 2015 revised curriculum. The analysis criteria were sub-elements of two subject competencies. Seven sub-elements were derived from each competency. Frequency analysis was performed to determine how often the sub-elements were reflected in the activity tasks. The results were as follows. First, with regard to the sub-elements of ‘practical problem-solving capability’, ‘value judgment’ was reflected most frequently in the activity tasks followed by ‘exemplification of solution’, ‘logical thinking’, ‘critical thinking’, ‘decision-making’, ‘practical reasoning’, and ‘evaluation of solutions’. Secondly, the sub-elements of ‘independent life capability’ were unevenly distributed in the activity tasks. The ‘capability to perform conscious living’ was reflected most frequently followed by ‘development and self-identity’, ‘time, money, and leisure management’, and ‘reasonable consumption and resource utilization’. For teachers wanting to teach activity-oriented classes and student participatory classes, the results pinpoint the materials necessary to develop learners’ subject competencies by using textbooks from different publishing companies.
Protecting Reward Function of Reinforcement Learning via Minimal and Non-catastrophic Adversarial Trajectory

Reward functions are critical hyperparameters with commercial values for individual or distributed reinforcement learning (RL), as slightly different reward functions result in significantly different performance. However, existing inverse reinforcement learning (IRL) methods can be utilized to approximate reward functions just based on collected expert trajectories through observing. Thus, in the real RL process, how to generate a polluted trajectory and perform an adversarial attack on IRL for protecting reward functions has become the key issue. Meanwhile, considering the actual RL cost, generated adversarial trajectories should be minimal and non-catastrophic for ensuring normal RL performance. In this work, we propose a novel approach to craft adversarial trajectories disguised as expert ones, for decreasing the IRL performance and realize the anti-IRL ability. Firstly, we design a reward clustering-based metric to integrate both advantages of fine- and coarse-grained IRL assessment, including expected value difference (EVD) and mean reward loss (MRL). Further, based on such metric, we explore an adversarial attack based on agglomerative nesting algorithm (AGNES) clustering and determine targeted states as starting states for reward perturbation. Then we employ the intrinsic fear model to predict the probability of imminent catastrophe, supporting to generate non-catastrophic adversarial trajectories. Extensive experiments of 7 state-of-the-art IRL algorithms are implemented on the Object World benchmark, demonstrating the capability of our proposed approach in (a) decreasing the IRL performance and (b) having minimal and non-catastrophic adversarial trajectories.
Attacking DNN-based Cross-modal Retrieval Hashing Framework with Adversarial Perturbations

The rapid development of Internet and online data explosions elicit strong aspirations for users to search for semantic relevant information based on available samples. While the data online are always released with different modalities like images, videos or texts, effective retrieval models should discover latent semantic information with different structures. Recently, the state-of-the-art deep cross modal retrieval frameworks have effectively enhanced the performance on commonly-used platforms using the deep neural networks (DNNs). Yet DNNs have been verified to be easily misguided by small perturbations, and there are already several attack generation methods proposed on DNN-based models for real-world tasks, but they are all focused on supervised tasks like classification or object recognition. To effectively evaluate the robustness of deep cross-modal retrieval frameworks, in this paper, we propose a retrieval-based adversarial perturbation generation method, and demonstrate that our perturbation could effectively attack the state-of-the-art deep cross-modal and single image retrieval hashing models.
Evaluation of Student Satisfaction with Ubiquitous-Based Tests in Women’s Health Nursing Course

Learning evaluation using ubiquitous-based tests may be essential during a public health crisis, such as the COVID-19 pandemic, during which theoretical classes and clinical practice are conducted online. However, students may not be as familiar with ubiquitous-based tests as they are with paper-based tests. This survey study aimed to evaluate students' satisfaction with ubiquitous-based tests and compare the evaluation results of a paper-based test with that of a ubiquitous-based test in nursing education. For the midterm exam of the Women's Health Nursing course, a paper-based test was conducted, while a ubiquitous-based test using a tablet computer was used for the final exam. The Ubiquitous-Based Test Usefulness and Satisfaction tool, which has a five-point Likert-type response scale, was employed to evaluate the post-test usefulness and satisfaction scores of the ubiquitous-based test. The mean score of the ubiquitous-based test usefulness was 4.01 ± 0.67. There was a significant difference in satisfaction levels between the ubiquitous-based and the paper-based test (t = -3.36, p = 0.001). Specifically, the evaluation scores were not affected by different evaluation methods. Study participants deemed the ubiquitous-based test highly useful and satisfactory, suggesting that such tests may be a future-oriented evaluation method, potentially replacing paper-based tests.
Edge detection of reflectance transformation imaging for colorless traces

Reflectance Transformation Imaging (RTI) is a computational photography technology that can enhance the legibility and the fine details of the sample. Colorless trace is a special kind of trace with shallow depth and its grayscale is similar to the background. It is difficult to perceive details of these traces by traditional observation and detection methods. In this paper, we propose some improved detection methods which combine RTI with the Canny algorithm to get more details of colorless trace. The threshold is one of the key factors which affect the performance of the Canny algorithm so we put forward a threshold selection method based on the gradient histogram of the image. Three detection methods that combine RTI with the improved Canny algorithm are proposed in this paper. The experimental results show that these detection methods can extract weak and colorless traces effectively, and would have great potential application in criminal investigation.
The era of computational lens design

Computerized ray-tracing has exponentially evolved since its inception. Various software companies have assisted the optical designer in optimization of lens designs. We introduce a fully automatic optimization method reducing optimization time ten-fold.
European Experience in Organization of Military Libraries in the Russian Empire

Due to the lack of comprehensive research in the area of use of the experience of military libraries in Europe, the article for the first time examines the ways of studying it and the forms of implementation when establishing military libraries in the Russian Empire. The purpose of the study is to identify the influence of Europe on the military librarianship in Russia. The author collected data from dozens of pre-revolutionary publications, articles from the military periodical press and regulatory documents that allowed to characterize the source base of the study as representative. The article presents the history of military libraries of European states. The results of research show that the main ways to obtain information about them were: the study of foreign military literature and the military periodical press; analysis of regulatory and legal documents (statutes, rules, manuals, regulations, catalogues) regulating the activities of these libraries; foreign business trips of officers and generals in order to familiarize themselves with the structure and functioning of foreign armies and their libraries; reports of Russian military agents; participation in international exhibitions of books and textbooks. The author revealed dozens of articles indicating that the experience of creating and operating of military libraries abroad was widely covered in the Russian military periodical press. The military Department of Russia closely followed these processes, adopting and implementing the best and useful of them taking into account Russian realities. The study of the creation of military libraries in Europe became a prerequisite for their organization in Russia. The European experience was reflected in the ways of budgeting and acquisition, in the forms of management and supervision over them, the formation of regulatory framework and in the variety of their types. The conducted research expanded the understanding of the state of military librarianship in European countries, about the ways of studying their experience by the Russian military Department and the forms of its practical application in the structure of military libraries of the Russian Empire.
Balinese Script Unicode Font: A Comparison Study

This study aims to analyze the accuracy of the Balinese script Unicode font. Currently, there are 4 Balinese script Unicode fonts namely Noto Sans, Vimala, Pustaka Bali, and Kadiri. These four fonts are available for free on the internet and could be used for the development of various applications, particularly those aimed at preserving the use of Balinese script. There was no study has yet conducted an analysis regarding the accuracy of these four fonts. These four fonts were examined by using them to write the Balinese Language Document, The Balinese Alphabet v0.6 by Ida Bagus Adi Sudewa, a document that was also widely used in previous studies. There were 186 cases in this document, covering all Balinese script glyphs with their variations. The result of this study is a suggestion for the next font development. From the 186 cases examined, the Vimala font succeed to write well as many as 93% (174 correct cases out of 186 cases); Pustaka Bali 93% (174 correct cases out of 186 cases); Kadiri 94% (175 correct cases out of 186 cases); and Noto Sans Balinese 66% (122 correct cases out of 186 cases).
Resource Selection Mechanism For Brokering Services In Multi-cloud Environment

Job scheduling plays a vital role as for resource management in cloud computing which will allocate the resources and schedule the jobs for execution. The resources' selection is a crucial part in resource broker for scheduling the incoming jobs. However, in the process of selecting the resources, there are scenarios which may occur such as overutilized and underutilized resources when processing the jobs. In this paper, we proposed a resource selection mechanism for brokering services in multi-cloud environment by adopting one of the machine learning technique called classification technique. The proposed mechanism will select the ‘best resources that can be used to process the jobs to increase resource utilization. We perform our simulations using CloudSim Toolkit. Simulation results demonstrate that our mechanism outperformed other mechanisms in terms of utilization rate of the resources, therefore provide improvement in resource usage.
An Efficient System to Collect Data for AI Training on Multi-Category Object Counting Task

This study focused on the problem of collecting data to train AI for a multi-category object counting task, which is like a standard object counting task, where we need to count the number of a particular type of object, for instance, counting how many people in an image. In multi-category object counting, we need to count more than one type of object by keeping track of the number of each type of object. Although in the real object counting case, we often find that AI needs to count objects with multiple categories, the dataset for that particular task is not publicly available. Meanwhile, to have a robust AI for this task, it needs to be trained with a massive amount of data. Therefore, in this study, we developed a system to efficiently facilitate massive data collection for multi-category object counting. This aim was achieved by a careful design of the user experience in the system. The system has been proved to be useful via Technology Acceptance Model (TAM).
Magnetic Hard Disk Serial Number Recognition Method Based on Machine Vision

Degaussing used magnetic hard drives is a common means of destroying magnetic information. Customized degaussing for different magnetic hard drives can improve the efficiency of magnetic information destruction. In this scenario, it is necessary to quickly identify the used magnetic hard disk firstly. Aim at the above problem, this paper proposes to apply machine vision to the identification of magnetic hard disk serial number. At first, the magnetic hard disk serial number image is preprocessed. Then, the serial number is segmented and its HOG features are extracted. Finally, a support vector machine (SVM) algorithm is used to construct a recognition model. The effectiveness of the proposed method is verified by the magnetic hard disk serial number image.
ELFIEP: Evolutionary Lifecycle Framework for Industrial Engineering Practice : A Ten Year Journey of Requirements Inspection Systems Design Methodology (RISDM)

This paper reflects on a decade-long journey into the industrial practices developed for requirements inspection in the Japanese software industry; it proposes an ELFIEP (Evolutionary Lifecycle Framework for Industrial Engineering Practice). We published two papers in RE’13 and RE’14. The first paper introduced the industrial practice of SRS (Software Requirements Specifications) inspection system of a software company. The system is a set of inspection items: inspection processes, techniques, and supporting tools. In the second paper, to encourage wider industrial adoption of requirements inspection systems, we proposed a methodology for designing requirements inspection systems. Called RISDM (Requirements Inspection System Design Methodology), it has been recognized by professional communities in Japan. A number of software companies have adopted the methodology, and are now operating their individual inspection systems. This paper provides a retrospect of the journey to RISDM. Learning from the journey, we propose ELFIEP as a generic framework for the evolution of engineering practice, and discuss insights gained from the journey and the framework.
Pedestrian Detection in Extreme Weather

Object detection is a computer vision technique that allows us to identify and locate objects in an image or a video. With this kind of identification and localization, object detection can be used to count objects in a scene and track their precise locations, all while accurately labelling them. The aim of this work is to detect pedestrians in extreme weather conditions like fog and mist, where the visibility is very poor. The main purpose is to prevent road accidents that occur due to visibility issues influenced by weather. Since the visibility is very low, instead of normal camera images, thermal images are used for the detection training and testing process. In this work, we have used YOLOv3 (You Only Look Once) and Faster RCNN (Faster Region-based Convolutional Neural Network). Thermal images are collected from the ZUT and OTCBVS repository. Later, an ensemble of both the aforementioned algorithms are used and its performance is analyzed. The results obtained using the three methods are compared, based on the evaluation metrics used.