Reviewerly/exHarmony huggingface dataset:
Research on the Method of Material Scheme Matching Based on Deep Learning

Based on the research of the deep learning network and the material scheme matching method, a material scheme matching method based on the combination of materials, solid ID data and multi-layer perceptrons is proposed. Based on the relationship among engineering design standards, general equipment selection and material procurement standards, a material and solidified ID data information system is formed. Then, collect, sort, and model electrical primary and secondary equipment and line information to form a model structure of plans and materials; finally, integrate and analyze historical data to form a typical plan material matching library. The training of the perceptron network obtains the material plan matching network. Experimental results show that the matching method of material schemes using materials, solidified ID data and multilayer perceptron network can achieve 96% matching accuracy, which solves the problem of information barriers in design standards, general equipment requirements and material procurement standards. The development of the material plan provides new ideas.
Research Ancient Artifact Identification Methods under Intelligent Perception and Recognition Technology

Over the last two decades, the identification of ancient artifacts has been regarded as one of the most challenging tasks for archaeologists. Chinese people consider these artifacts as symbols of their cultural heritage. The development of technology has helped in the identification of ancient artifacts to a greater extent. The study preferred machine-learning algorithms to identify the ancient artifacts found throughout China. The major cities of China were selected for the study and classified the cities based on different features like temple, modern city, harbour, battle, and South China. The study used a decision tree algorithm for recognition and gradient boosting for perception aspects. According to the findings of the study, the algorithms produced 98% accuracy and prediction in detecting ancient artifacts in China. The proposed models provide a good indicator for detecting archaeological site locations.
Optical distortion correction for an ultra-short throw with a wrinkled-surface target

Ultra-throw projector has been widely used in the projection application of large-FOV and short projection distance. However, the projection imaging distortion problem cannot be neglected due to the uncertain factor, such as the unsmooth factor of projection surface. In this paper, we proposed the image predistortion method based on radial basis function (RBF) image warping method to correct the projection distortion due to the unsmooth or wrinkled projection surface. The RBF image predistortion method and chief ray tracing method are used to realize a high-precision image predistortion. In addition, the distortion effect caused by the wrinkled projection surface with different depths are simulated. The distortion correction effects are also verified to less than 1% by the ray-tracing simulations.
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning

Human reasoning can often be understood as an interplay between two systems: the intuitive and associative (System 1) and the deliberative and logical (System 2). Neural sequence models -- which have been increasingly successful at performing complex, structured tasks -- exhibit the advantages and failure modes of System 1: they are fast and learn patterns from data, but are often inconsistent and incoherent. In this work, we seek a lightweight, training-free means of improving existing System 1-like sequence models by adding System 2-inspired logical reasoning. We explore several variations on this theme in which candidate generations from a neural sequence model are examined for logical consistency by a symbolic reasoning module, which can either accept or reject the generations. Our approach uses neural inference to mediate between the neural System 1 and the logical System 2. Results in robust story generation and grounded instruction-following show that this approach can increase the coherence and accuracy of neurally-based generations.
End-to-End Weak Supervision

Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels. Current state of the art approaches that do not use any labeled training data, however, require two separate modeling steps: Learning a probabilistic latent variable model based on the WS sources -- making assumptions that rarely hold in practice -- followed by downstream model training. Importantly, the first step of modeling does not consider the performance of the downstream model. To address these caveats we propose an end-to-end approach for directly learning the downstream model by maximizing its agreement with probabilistic labels generated by reparameterizing previous probabilistic posteriors with a neural network. Our results show improved performance over prior work in terms of end model performance on downstream test sets, as well as in terms of improved robustness to dependencies among weak supervision sources.
Face Mask Detection using Single Shot Multibox Detector

In 2019, COVID has originated in Wuhan, China and has rapidly proliferated across the globe. Since then, various governments have taken up measures such as Mask mandates, Social Distancing, frequent sanitization and providing emphasis on hygiene to contain the virus. In order to aid this effort and to identify citizens with facetious attitude towards emergency measures, who frequently refuse to wear face masks, we can use deep learning to help mitigate  this issue. Object detection has robust libraries of algorithms such as YOLO, SSD and R-CNN to detect face occlusions via consecutive feature  extraction  through  convolution  layers and feeding it to a detector. The experimental results  have been  done  in  real-time  application  and  the  device  has   been installed domestically for indoor usage.  The  experimental results shows that this technique can identify individuals who wear or do  not  wear  the  face  mask  precisely regardless of whether they are moving to a different position.
Distributed Computing Column 83 Five Ways Not To Fool Yourself

Overview. In this edition of the column, we have a very interesting and instructive contribution from Tim Harris (Microsoft, Cambridge, UK), whose goal is to provide a systematic overview of practical, "hands-on" techniques for the correct performance evaluation of a complex distributed system.
Path Planning for Autonomous Driving of Mobile Robots using Deep Neural Network based Model Predictive Control

A car-like mobile robot is a nonlinear affine system, and the mobile robot has physical constraints such as velocity and acceleration. Thus, no satisfactory solution may not be provided during self-driving under unknown environments. Although Model Predictive Control (MPC) has provided good performance in terms of control strategy, it is difficult to optimize the control parameters due to the uncertainty and non-linearity of a process. In this paper, the Deep Neural Networks (DNN) based Model Predictive Controller (MPC) is derived for tracking the given path during self-driving. The proposed DNN MPC produces the global optimal solution which has better performance than traditional MPC in terms of the errors of position and orientation. This paper verifies that the proposed DNN MPC based controller can track the desired path with high precision for the car-like mobile robot. Keywords—Path planning, autonomous driving, mobile robot, deep neural network, model predictive control.
FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations

Combining several embeddings typically improves performance in downstream tasks as different embeddings encode different information. It has been shown that even models using embeddings from transformers still benefit from the inclusion of standard word embeddings. However, the combination of embeddings of different types and dimensions is challenging. As an alternative to attention-based meta-embeddings, we propose feature-based adversarial meta-embeddings (FAME) with an attention function that is guided by features reflecting word-specific properties, such as shape and frequency, and show that this is beneficial to handle subword-based embeddings. In addition, FAME uses adversarial training to optimize the mappings of differently-sized embeddings to the same space. We demonstrate that FAME works effectively across languages and domains for sequence labeling and sentence classification, in particular in low-resource settings. FAME sets the new state of the art for POS tagging in 27 languages, various NER settings and question classification in different domains.
User Centered Design

However good a computer system is, it still needs to do what’s needed — not what we think is needed. User Centred Design finds out how people really use systems, and how to improve their experience and reliability.
Survey on Fake News or Truth - Rumours Detection using Machine Learning

This study is to help readers to understand detection of fake news using machine learning. The main purpose of the planned system is to build an application which identifies fake news stories from a bunch of news stories to make people aware of fake news rumours. With the help of machine learning algorithms, we can detect and separate out the fake news. Nowadays, it is become harder to identify the original source of news stories, like looking for a needle in a haystack. In the modern world, news is a kind of communication that keeps us up to date on the latest events, topics, and people in the wider globe. A society relies on news for a variety of reasons, the most important of which is informing its members about events taking on in and around them that might influence them. Oral and traditional media, as well as digital communication methods, altered videos, memes, unconfirmed marketing, and social media have all contributed to the spread of rumors. As nowadays many people use social media in many cases people get wrong and misleading information and people share those stories without verifying whether it is real or fake news stories. Spreading false information on social media has become a major problem these days. That is why we need a system that can tell us whether something is false news or not. Applications are: 1. Fake news may be detected on social media using this approach. 2. The system can be used to help news channels to broadcast only real and classified news. 3. Users can easily detect and eliminate fake articles that contain misinformation intended to mislead readers.
THE EFFECTIVENESS OF USING PARTICIPATION POINT SYSTEM (PPS) METHOD IN TEACHING ENGLISH AT THE ELEVENTH GRADE STUDENTS OF SMA MUHAMMADIYAH 9 MAKASSAR

Productive skill is one of types language skills. It is also known as active skill because it covers writing and speaking skill. Learners need to generate language to express their ideas either in speech or text. This research aimed at finding out the improvement of the students’ writing and speaking skill through Participation Point System (PPS) Method in teaching English. The researcher used pre-experimental design. The sample consisted of 20 students which was taken by using total sampling technique. The research findings showed that the students’ mean score in writing skill in terms of content was 48.30 pre-test to be 78.25 in post-test with 62.00% improvement. Then, the mean score in terms of organization was 45.80 pre-test to be 78.25 in post-test with 70.85% improvement. Meanwhile the students’ mean score in speaking skill in terms of pronunciation was 43.30 pre-test to be 67.55 in post-test with 47.23% improvement. and organization in post-test was higher than pre-test. Then, the students’ mean score in speaking skill in terms of pronunciation and vocabulary in post-test was higher than pre-test. The t-test analysis also showed that Sig.2-tailed = 0.005 which is smaller than α = 0.05. It means that there was a significant difference of students’ writing and speaking skill before and after using Participation Point System (PPS) Method. Based on the finding, it can be concluded that Participation Point System (PPS) Method was effective to improve students’ skill in teaching English at the eleventh grade students of SMA Muhammadiyah 9 Makassar.
Studies on Vertical Migration of Pollutants in the Soils of the Orenburg Technogenic Biogeochemical Province

The present study aims to estimate the parameters of vertical migration of chemical contaminants in the soils of the territory of the Orenburg technogenic province. The parameters of migration models of Cr, Ni, Cu, Pb, F in the soils of the Eastern Orenburg technogenic biogeochemical province are estimated. The enterprises of ferrous metallurgy, non-ferrous metallurgy, chemical industry are concentrated in this region, and there are many- year soil pollution. The results of field surveys of the 1980 s. Diffusion and convection- diffusion models were used. These models met the criteria of adequacy in most cases. The values of the diffusion parameters found are compared with the previously published values. The “apparent” diffusion coefficients are in the range (0.2-27) 10 -8 cm2 /s. Soil contamination forecast for 2020 is calculated. Calculated forecast values can be compared with the results of the next monitoring survey.
Utilization of Online Geographic Information System Learning Media towards Optimizing Geography Learning during the Pandemic Covid-19

This study aims to determine the effect of using online Geographic Information System learning media on student learning outcomes. This research method is a quantitative research with the type of experiment ( Quasy Experiment Design ). The population in this study were all students of class X consisting of X1 and X2 with a total of 62 students. The sample in this study was divided into two groups, namely the experimental group of 33 students and the control group of 29 students. This study uses data collection techniques using tests, interviews, documentation. The results showed that the calculation of the t-test obtained results Tcount = 4.853 with T table = 2.035. This means that Tcount > Ttable that H1 is rejected. Based on the results of the analysis calculation, it can be concluded that there is an influence of Geographic Information System media on student learning outcomes and students who receive learning with Geographic Information Systems media are better than before without using learning media.
Analyzing the Surprising Variability in Word Embedding Stability Across Languages

Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages. To gain further insight into word embeddings, we explore their stability (e.g., overlap between the nearest neighbors of a word in different embedding spaces) in diverse languages. We discuss linguistic properties that are related to stability, drawing out insights about correlations with affixing, language gender systems, and other features. This has implications for embedding use, particularly in research that uses them to study language trends.
Ontology-Based Indoor Domain Model Representation and Reasoning for Robot Path Planning Using ROS

Planning an optimal path for a robot is difficult in complex and uncertain environments. However, representing complex domain knowledge is necessary for performing robotic navigation. Hence, a robot requires a predefined map for efficient navigation. In order to perform complex tasks, avoid collisions, and accomplishing the goal, the robot needs semantic information about the domain model. Semantic information provides more abstract contextual information about the domain that enables the robot to perceive the domain accurately during navigation. This chapter presents a detailed explanation of an ontology-based model that represents an indoor environment efficiently. To improve the perception ability of mobile robots during semantic navigation, a three-layer perceptive autonomous navigation framework using ontology, planning domain and definition language, and a sampling-based algorithm is proposed. The proposed perceptive framework includes a region recognition model, reasoning inference model, and path classification model. This chapter explains these models in detail. Moreover, this chapter discusses the potential simulators used in implementing the proposed framework.
A study on selection property of university physical education classes using IPA method

The purpose of this study was to examine the importance and satisfaction of selection property of university physical education classes using IPA method.<BR> According to methods and procedures, the conclusions were as follow.<BR> As a result of analyzing the average difference between importance and satisfaction, there were significant differences in ‘responsibility and sincerity’ for the ability of instructors, ‘various experiences’, ‘acquisition(improvement) of motor skills’, and ‘promotion of exchange between students’ for lecture effects. And there were significant differences ‘the number of students per class’ for the lecture environment, and ‘safety of facilities and equipment’ for lecture facilities, and ‘program considering interest’ for the program.<BR> In the ranking analysis of the selection attributes, the importance was shown in the order of ‘Coping with Corona 19’, ‘Various experiences’, ‘Corona 19 prevention’, ‘Acquisition of exercise technology’, and ‘Time of use of facilities’. Satisfaction was expressed in the order of ‘Coping with COVID-19’, ‘Corona 19 Prevention’, ‘Providing exercise opportunities’, ‘Conversation with students’, and ‘Various experiences’. <BR> Quadrant I is an attribute with both high importance and satisfaction, Quadrant Ⅱ is an attribute of high importance but low satisfaction, Quadrant Ⅲ is an attribute of both importance and satisfaction low, and Quadrant Ⅳ is an attribute of low importance but high satisfaction.<BR> In this study, selection attributes were classified into quadrants Ⅰ, Ⅱ, Ⅲ, and Ⅳ through the importance and satisfaction of selection attributes, and through this, it can be used as basic data for establishing and developing college liberal arts physical education.
Refactoring and Code Fixes

In the previous chapter, we had a look at language features which are used to provide smart editing, documentation support, and diagnostics. When we consider language intelligence, another most useful set of features are refactoring features and code fixes. For example, most of the time the user tends to change variable names, adhere to formatting guidelines, and fix linter issues during the editing process. In this chapter, we are going to look at a set of language features exposed by the language server protocol to achieve refactoring and code fixing capabilities.
Geologic Time

The time scale of geology—the first overarching precept in geology—and its development are the focus of this chapter. How did geologists determine the great age of the Earth through the spatial nature of geologic units and changes in fossils over time? There was no guidebook to the process of unraveling the Earth’s biography, and the discoveries proceeded step by step using observation and the development of hypotheses. Scientists such as Abraham Werner established principles to place rocks in order relative to one another, providing the beginning of understanding strata, their composition, sources, and life within them. Early estimates of the age of the Earth were on the order of thousands of years, carefully calculated based on the generations in the Bible. However, geologists such as James Hutton and Charles Lyell realized that the probable age of the Earth was much greater by examining the time it would take for processes, like sedimentation rates for a layer of sand or mud to be deposited to occur. From these observations, they deduced it would take orders of magnitude more time to build up great masses of rock layers, and the time scale of geology was extended millions of years.
Extending Benefit Based Segmentation Techniques Performance Analysis Over Intensity Non Uniformed Brain MR Images

The bias field is an undesirable image foible that formulate during the process of image procurement. Segmentation is the procedure of segregating a digital image into constituent component or substantial segments which help in extracting quality amount of information from the region of interest. There is several bias correction strategies have been recommended till date, all these algorithms helps in reducing bias but none of them perfectly removes bias. When incorporating computer aided diagnosing in treatment planning, the leftover bias cause to inaccurate segmentation which leads to faulty diagnosis of the diseases. This paper scrutinizes the segmentation algorithms over bias corrupted brain MR Images and analyzes which segmentation algorithm efficiently segments the image components even though it is corrupted by bias field. The bench mark brain MR Images with different bias spectrum is employed for the research. Quantitative metrics are adopted to conclude the result. The outcome of this paper tends to provide accuracy in computer aided diagnosing and to elect appropriate segmentation technique while developing bias correction based segmentation algorithm.
Modeling Kramer’s Time Concepts

SummaryThis section implements our interpretation of the most significant of Kramer’s time concepts.
PERFORMANCE METHOD OF MAXIMUM DAILY RAINFALL FREQUENCY ANALYSIS USING CORRELATION

Rainfall in Bandar Lampung has a different distribution value for each station which is calculated to carry out the annual maximum daily rain forecast required in road planning, water building and drainage. Getting rain distribution values requires a method that meets the requirements of Cs, Ck and Cv. But this study reviews methods that have a correlation value close to the value of one. In this study, a validation test was carried out in advance to normalize values> 500 mm which were then calculated through the Normal, Log Normal, Gumbel and Log Pearson III methods. Look at the correlation value of each method with a scatter diagram. Analysis of the calculation of the rain distribution method at the Sukarame rain station (PH-003) Log Normal method has a value of 0.96 which has a correlation coefficient value that is closest to the value of 1 (one) among the other methods in all rain stations. Log Normal method has the best average correlation value that is 0.85 then the Normal method has a mean correlation value of 0.74 followed by Log Pearson III method with an average correlation value of 0.35 and the mean correlation value of the Gumbel method -0.01. The correlation value obtained using the Log Normal method is seen both at Sumberejo, Sukarame and Pahoman stations. However, at the Sumur Putri station the value of the Log Pearson III correlation, with the normal log method, the data correlation value is the best because the data fluctuations are close to the value of 1 compared to other methods. Keywords: Correlation, Rainfall, Normal Method, Log Normal Method, Gumbel Method, Log Pearson III Method.