Reviewerly/exHarmony huggingface dataset:
Real-Time On-Orbit Estimation Method for Microthruster Thrust Based on High-Precision Orbit Determination

In order to enable the micro-nanosatellites equipped with microthrusters to better complete various space applications, it is necessary to estimate the thrust performance of the microthrusters in real-time on orbit. This paper proposes a real-time on-orbit estimation method for microthrust based on high-precision orbit determination. By establishing a high-precision orbit dynamic model, the microthrust generated by a microthruster is modeled as a first-order Markov model, combined with a high-precision GNSS measuring device, and the satellite position is obtained through the cubature Kalman filter algorithm, velocity, and thrust real-time on-orbit estimates. For a thrust of 100 μN, the error accuracy of the on-orbit estimation is 3.98%; for a thrust of 500 μN, the error accuracy is 1.79%; for a thrust of 5 mN, the error accuracy can be reduced to 1.43%; and when the thrust is 500 μN, the accuracy of orbit determination is 16 cm. This method solves the problem that the traditional on-orbit thrust estimation method cannot perform real-time on-orbit estimation of microthrust on the order of hundreds of μN. The real-time on-orbit estimation of microthrust of micro-nanosatellites equipped with microthrusters of the order of hundreds of micronewtons or even several mN to tens of mN has certain reference value.
RESEARCH ON THE SETTINGS OF A COOL SPOT IN THE RADIATION COOLING/HEATING OFFICE

The last year our report describes the results of thermal environment settings about a cool spot installation in the radiation cooling/heating office through the subject examination of fitness and going-out action. In this research, we attached fine mist spraying area to cool spot. And the effectiveness evaluation was carried out considering “restoring time” from after-exercise sensation to original sensation. The feature of this research is that the attachment of the fine mist spraying area can reduce the number of the restoring time regardless of thermal environment settings of cool spots.
The Disadvantage of CNN versus DBN Image Classification Under Adversarial Conditions

We compare Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN) ability to withstand common image classification attacks.CNNs makes a strong inductive bias assumption about the relationship between pixels that are proximal to each other.We propose that this bias makes CNNs vulnerable to adversarial attacks.We implement two attacks using the MNIST and CIFAR-10 dataset where we modify pixels of the training and test images in different ways to challenge the CNN and DBN models.The results show that the DBN models generally perform better under attack than the CNN models.The CNNs convolutional inductive bias is at a disadvantage when the assumption of the relationship between proximal pixels no longer holds.
Credit Card Fraud Detection Techniques: A Review

The main problems within the credit card trade are ongoing fraud. The credit card has made our life easy as we can pay easily and move without carrying any cash. Credit card gains its popularity and utilization has dramatically inflated in our day to day life, for the speedy advancement of electronic commerce technology. However, the exploitation of credit card provides huge edges once used fastidiously and responsibly. Fraud activities are also increasing, and new techniques have been developed by criminals. Credit card and monetary damages are caused by fallacious activities. Such issues are tackled with Data Science, Machine Learning together with Deep Learning techniques, which cannot be exaggerated. This helps the bank and financial organizations, to detect the fraud at the early stage, and then they can reduce the ongoing fraud by not accepting the suspected transactions. The credit card company faces a huge loss if the cardholder does not detect the loss. An awfully very little quantity of data is needed by the assaulter for conducting any fallacious dealing in online transactions. During analysis work, numerous methods and outcomes are reviewed, in terms of definite parameters.
Mid-Air Hand Gestures for Post-Editing of Machine Translation

To translate large volumes of text in a globally connected world, more and more translators are integrating machine translation (MT) and post-editing (PE) into their translation workflows to generate publishable quality translations.While this process has been shown to save time and reduce errors, the task of translation is changing from mostly text production from scratch to fixing errors within useful but partly incorrect MT output.This is affecting the interface design of translation tools, where better support for text editing tasks is required.Here, we present the first study that investigates the usefulness of mid-air hand gestures in combination with the keyboard (GK) for text editing in PE of MT.Guided by a gesture elicitation study with 14 freelance translators, we develop a prototype supporting midair hand gestures for cursor placement, text selection, deletion, and reordering.These gestures combined with the keyboard facilitate all editing types required for PE.An evaluation of the prototype shows that the average editing duration of GK is only slightly slower than the standard mouse and keyboard (MK), even though participants are very familiar with the latter, and relative novices to the former.Furthermore, the qualitative analysis shows positive attitudes towards hand gestures for PE, especially when manipulating single words.
Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS

BPJS is really helpful because one of its goal is to provide good service for the member in terms of healthiness. But, when there’s many people using the service, then it will cause more pros and contras. Therefore, researcher will be doing sentiment analysis in the field of data mining towards bpjs users on social media Twitter as much as 1000 data that later will be filtered to be 903 data because there are some data that has been duplicated. Researchers used the KNN, Decision Tree, and Naïve Bayes methods to compare the accuracy of the three methods. Researchers used the RapidMiner version 9.7.2 tools. The results showed that the sentiment analysis of Twitter data on BPJS services using the KNN method reached an accuracy level of 95.58% with class precision for pred. negative is 45.00%, pred. positive is 0.00%, and pred. neutral is 96.83%. Then the Decision Tree method the accuracy rate reaches 96.13% with the precision class for pred. negative is 55.00%, pred. positive is 0.00%, and pred. neutral is 97.28%. And the last one is the Naïve Bayes method which achieves 89.14% accuracy with precision class for pred. negative is 16.67%, pred. positive was 1.64%, and pred. neutral is 98.40%.
Interactive Video Object Mask Annotation

In this paper, we introduce a practical system for interactive video object mask annotation, which can support multiple back-end methods. To demonstrate the generalization of our system, we introduce a novel approach for video object annotation. Our proposed system takes scribbles at a chosen key-frame from the end-users via a user-friendly interface and produces masks of corresponding objects at the key-frame via the Control-Point-based Scribbles-to-Mask (CPSM) module. The object masks at the key-frame are then propagated to other frames and refined through the Multi-Referenced Guided Segmentation (MRGS) module. Last but not least, the user can correct wrong segmentation at some frames, and the corrected mask is continuously propagated to other frames in the video via the MRGS to produce the object masks at all video frames.
LAMS: A Location-aware Approach for Multimodal Summarization (Student Abstract)

Multimodal summarization aims to refine salient information from multiple modalities, among which texts and images are two mostly discussed ones. In recent years, many fantastic works have emerged in this field by modeling image-text interactions; however, they neglect the fact that most of multimodal documents have been elaborately organized by their writers. This means that a critical organized factor has long been short of enough attention, that is, image locations, which may carry illuminating information and imply the key contents of a document. To address this issue, we propose a location-aware approach for multimodal summarization (LAMS) based on Transformer. We investigate image locations for multimodal summarization via a stack of multimodal fusion block, which can formulate the high-order interactions among images and texts. An extensive experimental study on an extended multimodal dataset validates the superior summarization performance of the proposed model.
An Automatic Pointer Meter Reading Method based on Deep Learning in Gas Gathering Station

Abstract pointer meters are widely used in gas gathering stations, and manual meter reading is time-consuming and laborious. This paper proposes an automatic reading method of pointer meters in gas gathering stations based on deep learning and image processing algorithm. Firstly, the input image is detected by YOLO neural network algorithm to quickly locate the position of the pointer meter in the image; then, the meter image is segmented from the background image, and after filtering and binarization preprocessing, the meter border is preliminarily detected by Hough transform. After further removing the background interference, the pointer area and scale value area are segmented by connected domain detection to detect. After measuring the center of mass of the scale value area, the center of the dial is corrected again by RANSAC fitting circle, and the pointer angle is identified by index table thinning algorithm, and finally the meter indication is calculated. The results show that this method can quickly locate the pointer meters in the gas gathering station under complex background and reduce the light interference.
GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction

As part of scientific articles, grant information refers to funder names and their corresponding grant numbers.Extracting such funding information from articles is of significant importance to both academic and funding bodies.The studies on this topic face two major challenges: 1) no high-quality benchmark datasets; and 2) difficulties in extracting complex relationships between funders and grantIDs.In this paper, we present a novel pipeline framework called GrantRel, which consists of a funding sentence classifier, as well as a joint entity and relation extractor.For this purpose, we manually label two highquality datasets called Grant-SP and Grant-RE, respectively.In addition, our relation extraction (RE) model uses both position embedding and context embedding in an adaptivelearning way.The experiment results have demonstrated that our model outperforms several state-of-the-art BERT-based RE baselines as higher as 6.5% of F1 scores against the PubMed Central (PMC) test set and 3.5% of that against the arXiv test set.
Keyword extraction method for machine reading comprehension based on natural language processing

Abstract The traditional keyword extraction method has the problem that the accuracy of identification and extraction decreases when the amount of data is increasing. This paper introduces natural language processing technology and designs a keyword extraction method for machine reading comprehension. In this process, considering that the information in the text of machine reading comprehension may come from the web page, we can refer to the NLP system of natural language algorithm. Firstly, analyze the content of a single document, then analyze the short string level, and finally organize the preprocessing of natural language text information, so as to achieve the preprocessing of machine reading and comprehension of Chinese text. At the same time, the SKEA keyword extraction algorithm system is designed to merge text words with the same meaning, locate the location information of document resources, and identify the annotated words in the resource base. After the annotation processing is completed, the annotation information is extracted. In addition, a comparative experiment is presented to prove the effectiveness of the proposed method compared with the traditional method.
Recommender Systems Algorithm Selection Using Machine Learning

This article delivers a methodology for recommender system algorithm selection using a machine learning classifier. Initially, statistical data from real collaborative filtering recommender systems have been collected to form the basis for a synthetic dataset since a real meta dataset doesn’t exist. Once the dataset has been developed a classifier can be applied to predict which recommender system among a range of algorithms will predict better for a given dataset. The experimental evaluation shows that tree-based approaches such as Decision Tree and Random Forest work well and provide results with high accuracy and precision. We can conclude that machine learning can be used along with a meta dataset comprised of statistical information in order to predict which recommender system algorithm will provide better recommendations for similar datasets.
Event-Detection Deep Neural Network for OTDR Trace Analysis

The Optical Time Domain Reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber using the measure of scattered or reflected light from points along the fiber. The resulting signal, namely the OTDR trace, is commonly used to identify and localize possible critical events in the fiber. In this work we address the problem of automatically detecting optical events in OTDR traces, and present the first 1D object-detection neural network for optical trace analysis. Our approach takes inspiration from a successful object detection network in images, the Faster R-CNN, which we adapt to time series domain. The proposed network can both classify and localize many optical events along an input trace. Our results show that the proposed solution is more accurate than existing software currently analyzing OTDR traces, improving the mean average precision score by $$27.43\%$$ . In contrast with existing solutions that are not able to distinguish many types of events, our algorithm can be trained in an end-to-end manner to detect potentially any type of optic event. Moreover, our network has been deployed on embedded OTDR devices to be executed in real-time.
Blended Learning Technology "Flipped Class" in the System of Higher Vocational Education

This article is devoted to the implementation of the blended learning technology " Flipped Class" into the educational process of higher vocational education. From the position of the systemic approach, the authors’ view on the development of the competitiveness of the future specialist with the help of traditional (classroom) and digital (distance) forms of training is revealed; the necessary pedagogical conditions for using the " Flipped Class " blended learning technology in higher vocational education are described. The authors present the peculiarity of organizing the educational process with the use of the " Flipped Class " technology. The article justifies the importance and relevance of this educational technology application due to the world socio-economic challenges. The authors of the study conclude that the implementation of the " Flipped Class" technology causes improvement of the forms and methods of the educational activity aiming professional development of future competitive professionals in the sphere of the agro-industrial complex. A structural model of organizing higher vocational education with the use of the " Flipped Class " blended learning technology was worked out and substantiated. The pedagogical conditions of using this technology are described in the article.
Stratigraphy, Geometry, and pattern of Imbricated zones, NW Zagros Fold and Thrust Belt in Iraqi Kurdistan Region

The High Folded and Imbricated zones are the formalized name represent the part of the Tectonic sub-division of Iraq and Kurdistan Region of Iraq (KRI). The study of compressional deformation and Stratigraphy are the keys to identify thrust faults structures and define the sequence of thrusting in High Folded and Imbrication zones as a part of Zagros Fold and Thrust Belts. Through this study, we try to describe the compressional thrust faults and stratigraphical unit description. Thrust faults make repetition of the sequences of Jurassic-Triassic Cretaceous sequence over thrust above Cretaceous sequences. In Iran-Iraq border close to Sirwan valley the large thrust faults (Sirwan-Sazan Thrust faults) identify. The fault system makes Triassic formations over-thrust the Valanginian-Albian Balambo Formations. In Balakayati area serious thrust fault exists, which creates twice repetition of Jurassic-Cretaceous formations forming imbrication thrusting system and fault propagation fold. In Bradost-Korek anticlines the Jurassic-Cretaceous formations over-thrust Cenozoic formations. In Kamosk anticline the thrust faults are in the Cretaceous formations. The dimension and the style of the thrust faults are different but generally, most of the decollement layers of the thrusting are the Early Triassic and Middle-Late Jurassic formations. The collection of all these results constrain geological cross-section in Imbricate Zone and restore to obtain the 7.5 km distance of Shortening due to folding and faulting.
Single-state state machines in model-driven software engineering: an exploratory study

Abstract Context Models, as the main artifact in model-driven engineering, have been extensively used in the area of embedded systems for code generation and verification. One of the most popular behavioral modeling techniques is the state machine. Many state machine modeling guidelines recommend that a state machine should have more than one state in order to be meaningful. However, single-state state machines (SSSMs) violating this recommendation have been used in modeling cases reported in the literature. Objective We aim for understanding the phenomenon of using SSSMs in practice as understanding why developers violate the modeling guidelines is the first step towards improvement of modeling tools and practice. Method To study the phenomenon, we conducted an exploratory study which consists of two complementary studies. The first study investigated the prevalence and role of SSSMs in the domain of embedded systems, as well as the reasons why developers use them and their perceived advantages and disadvantages. We employed the sequential explanatory strategy, including repository mining and interview, to study 1500 state machines from 26 components at ASML, a leading company in manufacturing lithography machines from the semiconductor industry. In the second study, we investigated the evolutionary aspects of SSSMs, exploring when SSSMs are introduced to the systems and how developers modify them by mining the largest state-machine-based component from the company. Results We observe that 25 out of 26 components contain SSSMs. Our interviews suggest that SSSMs are used to interface with the existing code, to deal with tool limitations, to facilitate maintenance and to ease verification. Our study on the evolutionary aspects of SSSMs reveals that the need for SSSMs to deal with tool limitations grew continuously over the years. Moreover, only a minority of SSSMs have been changed between SSSM and multiple-state state machine (MSSM) during their evolution. The most frequent modifications developers made to SSSMs is inserting events with constraints on the execution of the events. Conclusions Based on our results, we provide implications for developers and tool builders. Furthermore, we formulate hypotheses about the effectiveness of SSSMs, the impacts of SSSMs on development, maintenance and verification as well as the evolution of SSSMs.
Generating Reliable Process Event Streams and Time Series Data Based on Neural Networks

Domains such as manufacturing and medicine crave for continuous monitoring and analysis of their processes, especially in combination with time series as produced by sensors. Time series data can be exploited to, for example, explain and predict concept drifts during runtime. Generally, a certain data volume is required in order to produce meaningful analysis results. However, reliable data sets are often missing, for example, if event streams and times series data are collected separately, in case of a new process, or if it is too expensive to obtain a sufficient data volume. Additional challenges arise with preparing time series data from multiple event sources, variations in data collection frequency, and concept drift. This paper proposes the GENLOG approach to generate reliable event and time series data that follows the distribution of the underlying input data set. GENLOG employs data resampling and enables the user to select different parts of the log data to orchestrate the training of a recurrent neural network for stream generation. The generated data is sampled back to its original sample rate and is embedded into the originating log data file. Overall, GENLOG can boost small data sets and consequently the application of online process mining.
The Implementation of DCGAN in the Data Augmentation for the Sperm Morphology Datasets

A large amount of data is the key requirement in order to train a neural network efficiently.Using a small size training set in network training causes low accuracy for model performance over the testing set and also hard to implement the model in practice.Similar to many other problems, sperm morphology datasets are also limited for training the neural network-based deep networks in order to provide an automatic evaluation of sperm morphometry.Data augmentation mitigates this problem by utilizing actual data more effectively.The standard data augmentation techniques focus on only spatial changes over the images and can only produce a restricted number of useful informative and disjunctive data.Therefore, in order to create more distinctive and diverse data than the regular spatial domain-based augmentation techniques, a deep learning-based data augmentation technique which is known as the generative model, is trained in this study for the sperm morphology datasets.The deep convolutional generative adversarial network (DCGAN) was optimized and utilized in this study for three well-known sperm morphometry datasets as SMIDS, HuSHeM, and SCIAN-Morpho.Each dataset was individually augmented to a 1000 sample size by the proposed approach.In order to optimize the network with different parameters and observe the generated data, a graphical user interface has been designed.For the similarity evaluation of the generated images to original images, the Fréchet Inception Distance (FID) score was utilized.The FID results indicate that the most similar generated images have been obtained for SMIDS with an average of 29.06 FID score.The worst performance (Average FID = 53.46)was obtained for the SCIAN-Morpho dataset, which has low resolution and data imbalance problems.Lastly, DCGAN based proposed approach resulted in an average of 44.25 FID score for the HuSHeM dataset.