Reviewerly/exHarmony huggingface dataset:
Fast Image-Anomaly Mitigation for Autonomous Mobile Robots

Camera anomalies like rain or dust can severely degrade image quality and its related tasks, such as localization and segmentation. In this work we address this important issue by implementing a pre-processing step that can effectively mitigate such artifacts in a real-time fashion, thus supporting the deployment of autonomous systems with limited compute capabilities. We propose a shallow generator with aggregation, trained in an adversarial setting to solve the ill-posed problem of reconstructing the occluded regions. We add an enhancer to further preserve high-frequency details and image colorization. We also produce one of the largest publicly available datasets <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> to train our architecture and use realistic synthetic raindrops to obtain an improved initialization of the model. We benchmark our framework on existing datasets and on our own images obtaining state-of-the-art results while enabling real-time performance, with up to 40x faster inference time than existing approaches.
An Alternative Approach for Thai Automatic Speech Recognition Based on the CNN-based Keyword Spotting with Real-World Application

An automatic speech recognition (ASR) is a key technology for preventing an ongoing global coronavirus epidemic.Due to the limited corpus database and the morphological diversity of the Thai language, Thai speech recognition is still difficult.In this research, the automatic speech recognition model was built differently from the traditional Thai NLP systems by using an alternative approach based on the keyword spotting (KWS) method using the Mel-frequency cepstral coefficient (MFCC) and convolutional neural network (CNN).MFCC was used in the speech feature extraction process which could convert the voice input signals into the voice feature images.Keywords on these images could then be treated as ordinary objects in the object detection domain.The YOLOv3, which is the popular CNN object detector, was proposed to localize and classify Thai keywords.The keyword spotting method was applied to categorize the Thai spontaneous spoken sentence based on the detected keywords.In order to find out the proposed technique's performance, real-world tests were carried out with three connected airport tasks.The Tiny-YOLOv3 showed the comparative results with the standard YOLOv3, thus our method could be implemented on the low-resource platform with low latency and a small memory footprint.
RF-Ubia: User Biometric Information Authentication Based on RFID

Traditional authentication technologies usually perform identity authentication based on user information verification (e.g., inputting the password) or biometric information (e.g., fingerprints) for identity authentication. However, there are security risks when these authentication methods are applied solely. For example, if the password is compromised, it is unlikely to determine whether the user is legitimate based on the password. In this paper, we propose RF-Ubia, which combines user information and biometric features to double guarantee the security of identity authentication. The RF-Ubia is a user identification system composed of an array of nine passive tags and a commercial RFID reader, which firstly verifies the user’s password, and then identifies the biometric characteristics of the legitimate user. Due to the coupling effect among tags, any tag signal change caused by the user’s touch operation will affect other tag signals at the same time. Since each user has different fingertip impedance, their touch will cause a unique change of tag signal. Therefore, by combining biometric information, the tag array will uniquely identify users. Evaluations results show that RF-Ubia achieves excellent authentication performance with an average recognition rate of 92.8\({\%}\).
Magnetic and radiometric signatures applied to exploration of vermiculite mineralized mafic-ultramafic intrusions in Central Brazil

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsMagnetic and radiometric signatures applied to exploration of vermiculite mineralized mafic-ultramafic intrusions in Central BrazilAuthors: Lucas Portes RamosMarcelo Leão-SantosYaoguo LiEduardo Cavalcanti CamposLucas Portes RamosUniversidade Federal de GoiásSearch for more papers by this author, Marcelo Leão-SantosUniversidade Federal de GoiásSearch for more papers by this author, Yaoguo LiColorado School of MinesSearch for more papers by this author, and Eduardo Cavalcanti CamposBrasil MinériosSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3594875.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractVermiculite is an industrial mineral used mainly in building construction and agriculture and also used as anticorrosive and thermal and acoustic insulator. Vermiculite mineralization is associated with structurally controlled hydrothermal alteration processes in phlogopite-rich mafic-ultramafic intrusions hosted by granodioritic orthogneisses. In this research, we use airborne magnetic and radiometric data to characterize known deposits and select possible targets in the study region. First, we measured magnetic susceptibility on drill cores to define ore correlation with magnetic responses. Second, we selected known deposits to establish airborne geophysical signatures related to the mineralization. Magnetic data was used to enhance shallow sources and map intrusion extents and fertile structural zones. Radiometric data was used to map mafic-ultramafic rocks of low responses. Magnetic anomaly amplitudes were quantified and correlated to three types of known deposits. Based on the correlation, 59 potential targets were selected and prioritized.Keywords: magnetic susceptibility, mining, South America, radiometrics, airborne surveyPermalink: https://doi.org/10.1190/segam2021-3594875.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Lucas Portes Ramos, Marcelo Leão-Santos, Yaoguo Li, and Eduardo Cavalcanti Campos, (2021), "Magnetic and radiometric signatures applied to exploration of vermiculite mineralized mafic-ultramafic intrusions in Central Brazil," SEG Technical Program Expanded Abstracts : 1285-1289. https://doi.org/10.1190/segam2021-3594875.1 Plain-Language Summary Keywordsmagnetic susceptibilityminingSouth Americaradiometricsairborne surveyPDF DownloadLoading ...
Level Target Inject Net for IDs Representation in Recommendation System

For a recommendation task, there are many unordered discrete ID attributes in a dataset like user ID and item ID. They cannot be applied to a model directly owing to the sparse and highly dimensional problem. In the existing methods, they are embedded into a low-dimension vector only considering the relationship of IDs, and most of these methods are time-consuming for multi-classification. In this paper, we propose a training framework based on neural network to learn entity ID representation, which can make full use of these IDs to enhance the recommendation performance while avoiding the inconvenience mentioned above. Firstly, we choose a suitable network for a certain task, and for each ID we calculate a statistical value associated with the task label. To reduce the impact of those IDs with strong label-distinguishing ability, we train the model in different levels: for the ID sequence in a training sample, we mask different IDs to calculate their average statistical value as a dynamic labels in different levels. To saving time for training a multi-class model, we use the information of unmasked IDs to predict the average statistical value of the masked IDs instead of the masked ID, which enables the target information to be injected into the ID representation and considers the relationship between ID and label besides the relationship of IDs. Without other steps required, the model will predict task label by masking no IDs at the last level. Our experiments on two datasets illustrate the advantages of our method compared with some state-of-the-art ID embedding methods and we won runner-up by applying this method successfully in Tencent Competition 2020.
High latitude ground truthed assessment of NSRDB V3 irradiance using sky condition data

We perform a global horizontal (GHI) and direct normal (DNI) surface solar irradiance comparison between the National Solar Radiation Database (NSRDB V3) and four Canadian Solar Spectral Irradiance Meter Network (CanSIM) stations for 2018-2019. On average, under all-sky conditions, the GHI from the NSRDB had the mean absolute error (MAE) and the root mean square error (RMSE) of 14% and 18%, respectively, while for the DNI those metrics were 15% and 24%, respectively. By using the sky condition classification obtained from a spectral pyranometer, we estimated that 7% to 17% (depending on location studied) of NSRDB cloudy sky data is misattributed as such due to satellite parallax and snow cover errors. The lower bound on parallax error varied from 5% and 12%, while the upper bound on the cloud-snow discrimination error ranged from 2% to 5%.
Comparative Study on Image Classification Algorithm Based on Dimensionality Reduction Technologies and Convolutional Neural Networks

As the convolutional neural network is widely used in digital image processing , natural language processing, etc, it is facing more and more challenges. The convolutional neural network is designed to be more complex, and the amount of data required to train convolutional neural network has also grown substantially, which leads to higher time cost and hardware requirements for training convolutional neural network. This paper puts forward a new solution in order to solve this problem . In this paper, the whole image classification task is regarded as a competition, each feature in the image is regarded as an individual contestant, the image dimensionality reduction technology used in the model is regarded as a preliminary contest, and the convolutional neural network in the model is regarded as a final. The image processed by dimensionality reduction technology is input to CNN(Convolutional Neural Network). In order to verify the idea of this paper, we design two convolutional neural network architectures : model 1 and model 2. From the experimental results in this paper, it can be seen that the accuracy of the model 1 training dataset decreases with the increase in dimensionality reduction and approaches to 86.508% of the accuracy of training dataset without dimensionality reduction; the accuracy of model 2 training dataset is not sensitive to the selection of dimensionality reduction range and dimensionality reduction method and fluctuates within 7.126% as a whole; the accuracy and time cost of the test dataset of model 1 and model 2 are greatly affected by the dimensionality reduction method and dimensionality reduction range.The final experimental results show that the loss of accuracy can be reduced to an acceptable range, and the dimensionality reduction operation is very meaningful for the reduction of time cost.
A Global Database For Quantifying Predictability of Solar Irradiance

Solar forecast is an essential subject in many engineering and geophysical fields. It is well known that the accuracy of solar forecasts may vary considerably across different geographical locations and temporal periods. In order to evaluate one’s solar forecast, the root-mean-square error (RMSE) score has been widely used to compare the performances of different forecast models across various locations and time periods. Alternatively, for the same evaluation purpose, one may also employ a satellite-derived or reanalysis irradiance database with global coverage. In this paper, we propose such a global database of reference solar forecast accuracy using records from the Clouds and the Earth’s Radiant Energy System over the time period 2000 to 2020. The final RMSE score can be calculated using the corresponding reference forecasts in our database, and thus provides a worldwide guideline for solar forecast research. Furthermore, We also discovered an interesting relation between RMSE on a global scale and the classification of climate.
Predicting User Preferences via Heterogeneous Information Network and Metric Learning

Heterogeneous information network contains richer semantic information, considering multiple types of objects and relationships to more accurately determine user preferences. In addition, existing approaches usually project each user to a point in the space, it is insufficient to accurately model the intensity of the user-item relationship and the heterogeneity of different types of objects and their relationships in implicit feedback. In order to solve these problems, we propose Predicting User Preferences via Heterogeneous Information Network and Metric Learning (PUHML). First, we use heterogeneous information networks to model complex heterogeneous data, and obtain users and item node representations. Second, we construct a user-item relationship vector, and translate each user toward items according to the user-item relationship. Finally, to alleviate the limitation of inner product as a scoring function, we introduce metric learning instead of dot product, and use distance to measure user preferences. Experimental results on three datasets demonstrates the effectiveness of our proposed approach over some competitive baselines.
Multi-Channel Audio Dispersion Using Programmable Control

This paper presents a 6ch multi-channel non-ambisonic sound dispersion system utilizing of programmable control of sound location as well as other parameters. We have adapted this control system for a sound-art installation using national anthems generated by a variety of sources, with the choice of anthem, playback speed, spatial location and other parameters being under program control.
Construction of national geo-hazard monitoring and early warning platform

In order to meet the needs of transmission, management and analysis of geo-hazard monitoring data, a national geo-hazard monitoring and early warning platform is constructed to realize high integration of the platform, standardized access of monitoring equipment, real-time transmission, analysis and early warning, decision -making and disposal of monitoring data. It supports dynamic data scheduling among different levels of national-provincial-city-county, full two-way sharing of data, and coordination and linkage with other disaster prevention systems. The construction of the platform further improved the scientific, information, standardization and visualization of the national geo-hazard monitoring and early warning work. This paper mainly introduced the construction scheme of national geo-hazard monitoring and early warning platform.
From Reflection to Self-Assessment: Methods of Developing Critical Thinking in Students

The main task of a higher education institution is to prepare a student for professional duties in a rapidly changing world. To effectively perform this task, it is necessary to give students not only knowledge but also to develop the necessary skills and abilities, in particular to develop critical thinking. The purpose of this work was to develop a methodology for the development of critical thinking of students, as well as to investigate its effectiveness. The study used the method of synthesis, which developed a method of developing critical thinking of students. To diagnose the effectiveness of the proposed method used the test "Critical Thinking" L. Starkey adapted by O. Lutsenko. In the study the peculiarities of the development of such critical thinking skills as reflective thinking, self-analysis, awareness of one’s own achievements and shortcomings, choice of problem-solving strategy, use of cognitive models of learning (Socratic dialogue, Paul &amp; Elder taxonomy) are revealed. It was found that the development of critical thinking was achieved through a comprehensive combination of self-assessment and reflection, performing exercises to develop the ability to clearly articulate the problem, find, analyse and interpret relevant information, draw the right conclusions and explanations.The results of this study can be useful to researchers and university professors who are looking for effective methods of developing critical thinking in students.
Research on Multi Strategy intelligent interactive translation and related issues on mobile platform

Based on the existing research results, this paper studies the Multi Strategy intelligent interactive translation and related issues on the mobile platform. In Multi Strategy intelligent interactive translation, a variety of models are used, including translation model, language model, sentence length penalty model, reordering distance limit model, lexicalized reordering model. In the Multi Strategy intelligent interactive translation, the decoding method of left to right style is mainly used, that is, the source language phrases are constantly selected for translation, and the target language sentences are continuously generated in the way of left to right. The reason why we adopt this left to right approach is that it just meets the matching requirements of translation prefix. Based on the mobile platform, the interactive mode of Multi Strategy intelligent interactive translation is designed. The traditional interactive process is divided into three processes: "phrase splitting - phrase translation item confirmation - re Decoding". The interpreter can directly select the correct translation item for the source language in the loaded phrase translation item list to guide the computer decoding. The Hong Kong Chinese English Parallel Corpus of LDC20001 is selected as the experimental corpus for the comparative experiment. The experimental results show that the proposed method can reduce the translation time, reduce the cognitive burden, reduce the translation time and improve the translation efficiency.
On the merits of checklists for catalyst researchers

The Annamite Mountains extend from northern Viet Nam along the border area with Lao PDR and south into southern Viet Nam. The region is a biodiversity hotspot renowned for its high level of endemic species of fauna and flora, variety of ecosystems, and characteristic geophysical and climatic patterns. The Nakai – Nam Theun National Park, located in east central Lao PDR within the Annamite Mountains, provides a case study of the rich biodiversity of the region and the threats it is facing. Many large wildlife species have already gone extinct from Nakai – Nam Theun in the past two decades. This is due to the intensive trade-driven wildlife hunting that is widespread in the region. However, Nakai – Nam Theun is still considered one of the most important protected areas for its conservation significance at the national and global level, as well as for its diversity of human ethnic minorities. The conservation of this highly diverse area will only be possible with a strong commitment from the government of the Lao PDR for long-term management actions.
TroBo: A Novel Deep Transfer Model for Enhancing Cross-Project Bug Localization

Bug localization, which aims to locate buggy files in the software project by leveraging bug reports, plays an important role in software quality control. Recently, many automatic bug localization methods based on historical bug-fix data (i.e., bug reports labeled with corresponding buggy code files) have been proposed. However, the lack of bug-fix data for software projects in the early stages of development limits the performance of most existing supervised learning methods. To address this issue, we propose a deep transfer bug localization model called TroBo, which can transfer shared knowledge from label-rich source project to the target project. Specifically, we accomplish the knowledge transfer on both the bug report and code file. For processing bug reports, which belong to informal text data, we design a soft attention-based module to alleviate the noise problem. For processing code files, we apply an adversarial strategy to learn the project-shared features, and additionally extract project-exclusive features for each project. Furthermore, a project-aware classifier is introduced in TroBo to avoid redundancy between shared and exclusive features. Extensive experiments on four large-scale real-world projects demonstrate that our model significantly outperforms the state-of-the-art techniques.
Digital Humanity for Nepali University Teachers

I postulate that if we harness digital humanity, humanities education can strike back in the world. I present a perspective on globalization and humanities from a location, which perhaps enjoys a little reputation in terms of the volume of humanistic education. I presently belong to a department in Kathmandu University School of Management. Being a language and communication faculty, I also give some literature and composition lectures and run workshops across several schools. One of the benefits is that I can do needful research there besides having the rights to design and update the syllabus.
Analysis Of Accounting Information For The Purchase Of Raw Materials And Inventory Of Raw Materials

This research was conducted with the aim of proving the implementation of the accounting system for raw material inventories and purchasing of raw materials at the charcoal company PT. Karang Pilang Agung, in addition to monitoring problems that occur in accounting system at PT. Karang Pilang Agung, and to analyze whether the accounting information system for purchases, raw material inventory applied by PT. Karang Pilang Agung is in accordance with the accounting information system theory.&#x0D; In this research, qualitative approach was used. The data methods used were interviews, documentation, and observation. The interview method is used to find more in-depth information with the stage of giving questions to the general section and the expert section. The documentation method is used to corroborate the data in the triangulation process. The observation method is used to collect data with information notes during the research directly in the factory. The data analysis technique used is qualitative with the triangulation method.&#x0D; The results showed that the accounting information system for raw material inventory applied at PT. Karang Pilang Agung has been going well. This is evidenced by the selection of each supplier, and checking the quantity and specifications of raw materials or goods that come by involving various functions with their respective duties and responsibilities and have been equipped with documents that can be accounted for. The results of the research on the accounting information system for the purchase of raw materials are also going very well, with the Quality Control (QC) section and also each new supplier will be selected according to company criteria.