Reviewerly/exHarmony huggingface dataset:
Height Deviation Detection of Rail Bearing Platform on High-Speed Railway Track Slab Based on Digital Image Correlation

The security and convenience of high-speed railway depend on the high reliability and high stability of the ballastless track. To ensure the track regularity, it is necessary to measure the height deviation of each rail bearing platform before laying the steel rail. Through adjusting the fastening system, the rail could reach the design height and be in the same level. Conventionally, the height deviation of the bearing platform is measured through the contact walking measurement system. However, there are shortcomings such as few measurement points, long preparation time and low efficiency. In this paper, a fusion measurement method based on digital image correlation method and total station is proposed to solve these shortcomings. A measuring equipment consisting of the dual cameras and the reflecting prisms is assembled to measure the height deviation of bearing platforms (675 mm × 275 mm × 28 mm) on the high-speed rail track slab (5600 mm × 2500 mm). The three-dimensional contour of each bearing platform is measured using three-dimensional digital image correlation method and unified by locating the position of the reflecting prisms using the total station. In real application, the height deviation of each bearing platform is measured with high accuracy, the contour measurement accuracy of a single bearing platform is better than 0.1 mm and the location accuracy of the multiple bearing platforms is better than 0.5 mm. More importantly, the range of ballastless track with bearing platforms to be detected in a single day is increased from the original 500 m to 5000 m under the premise of ensuring accuracy. The efficiency is increased by 10 times. The paper achieved the height deviation detection of the rail bearing platform with high-precision and high-efficiency, contributing to improve the construstion efficiency.
Speed of Catch-up and Convergence of the Artificial Intelligence Divide: Robotic and Patents

This study examined the dynamics of the AI divide on robotics and AI patents from 42 to 57 countries grouped in the two subgroups of income and AI adoption levels. The most important finding is that for robotics, a very large relative AI divide which existed at the beginning year between the leading and lagging countries has narrowed very rapidly. The relative AI divide between the high- and middle-income subgroups at the beginning has also narrowed at a rapid pace. In contrast, a smaller relative AI divide existing at the beginning for patents experienced significantly slower pace of narrowing relative digital divide. The theory of convergence applies extremely well so that widely variable initial relative divides at the beginning year in the study period are related to the variable annual speed of catch-up rates, which, in turn, are associated with the different annual reduction rates of the relative divide.In contrast, actual penetration numbers of robotics and patents are substantially higher by the top nine leading countries over the bottom line lagging countries as well as by the high income countries over the middle income countries. Furthermore, these actual higher penetration numbers continued to increase in spite of continuous reduction of relative digital divide. Thus, multiple years will still be needed to equalize the varying levels on the actual number of AI adoption between the leading and lagging subgroups of countries as well as between the high- and middle-income subgroups of countries.In terms of the micro convergence analysis of AI divides for individual countries within the respective income and adoption subgroups, active ranking changes among countries occurred in nearly all the subgroups. In general, reduction rates of relative AI divide from macro analysis were supported by rapid ranking changes among individual countries from micro analysis.
Paradigm of Formation of Economic Competence of a Teacher of Professional Training in the Economic Profile

This article is devoted to creation of paradigm of formation of economic competence of a teacher of professional training in the economic profile. Economic competency is determined by a set of theoretical knowledge and practical skills. Economic competence is determined by the knowledge base and provided with skills for their practical application. This becomes a determining factor in further professional implementation, life. Therefore, it can be argued that the economic competence of a graduate of a higher school is a means of building a life trajectory, career. One of the main tasks of this diagnostics is to develop the criteria of the studied competence and the didactic possibilities for its improvement. In order to monitor the formation of economic competencies of the future teacher of vocational training, we have taken as a basis a three-level scale, which determined the low, medium, and high levels. An increase in the general competence as a generalized result of the formation of competencies by type of activity occurred under the influence of the set of pedagogical conditions that we identified, therefore, only with their comprehensive implementation is it possible to develop the economic competence of future professional education teachers.
Role of Mafic Replenishments in the Geochemical Evolution of the Deer Isle Granite, Coastal Maine

The chemical and textural evolution of a magma body can be greatly impacted by the interaction of granitic and mafic magmas. Here we present an evolutionary model for the petrogenesis of the Deer Isle granite based upon mineral and rock textures and new whole rock geochemical and isotopic data that indicate such an interaction played a significant role during the evolution of this magma. A granitic magma with mantle isotopic signature was produced through partial melting of the predominantly Avalonian-affinity lower crustal rocks. During emplacement, this granitic magma partially crystallized and mixed and mingled with a mafic, mantle-derived melt, forming intermediate composition magmatic inclusions. A mixture of plagioclase, orthoclase, hornblende, and biotite crystals within the crystal mush settled to form the basal cumulate-style facies after the magma was fully emplaced. Following emplacement, the upper portion of the chamber formed due to the downward growth of a solidification front. The interior portion of the chamber remained partially liquid the longest and interacted with a second mafic input, forming and distributing mixed magma inclusions within a highly heterogeneous textured granite.
Influence of Parent Attention, Peer Environment, and Learning Motivation on Learning Achievement of Students of Office Administration Education Faculty of Economics Jakarta State University for 2019/2020

The purpose of this study was to determine the effect of parental attention, peer environment, and learning motivation on student achievement in Office Administration Education, Faculty of Economics, State University of Jakarta. The data collection method used a survey method with the instrument in the form of a questionnaire. This study used a sample of 119 respondents from Office Administration Education students class of 2019 and 2020. To process and analyze research data, the data analysis technique usedSmart PLS 3.3.7 application program. Using the Structural Equation Modeling (SEM) approach. The results showed that the variables of parental attention, peer environment, and learning motivation had a positive effect on learning achievement in Jakarta State University Office Administration Education students batch 2019 and 2020.
Approximate Function Classification

Classification of Boolean functions requires specific software or circuits to determine the class of a function or even to distinguish between two different classes. In order to provide a less costly solution, we study the approximation of the NPN function classification by a artificial neural network (ANN), and shown that there are configurations of ANN that can perfectly classify four-bit Boolean functions. Additionally, we look at the possibility of learning the classification of four-bit Boolean functions using a set of three-bit Boolean neural classifiers, and determine the scalability. Finally we also learn a discriminator that can distinguish between two functions and determine their similarity or difference in their NPN classes. As a result we show that the approximate neural function classification is a convenient approach to implement an efficient classifier and class discriminator directly from the data.
A Similarity Measure for Formal Languages Based on Convergent Geometric Series

We present a distance metric on formal languages based on the accumulated weight of words in their symmetric difference. The contribution of an individual word to this weight decreases exponentially in its length, guaranteeing the distance between languages to be a real value between 0 and 1. We show that this distance is computable for regular languages. As an application, we show how the similarity measure derived from a modification of this metric can be used in automatic grading of particular standard exercises in formal language theory classes.
Generative models for synthesis of colorectal cancer histology images

The generation of synthetic cancer tissue images has become widely popular over the last few years to understand the underlying morphological characteristics as well as tumor heterogeneity in cancer tissues. They can be potentially instrumental in training and evaluating digital pathology algorithms, particularly where data availability is limited. Since the collection and subsequent annotation of stained histopathology slides is laborious, replacing them with synthetically generated and annotated slides can significantly reduce the annotation burden. In this chapter, we describe in detail two published methods for generating synthetic histology images of colorectal cancer tissues. The first method presents a spatial model named TheCoT, which models the tumor heterogeneity in colorectal cancer tissues. It offers flexibility to control the appearance of the tissue images based on user-defined parameters such as cancer grade, cellularity, cell-overlap ratio, objective level, and image resolution. The second method proposes a deep learning based framework to generate large colorectal cancer tissue tiles of arbitrary sizes, exhibiting the realistic appearance of glands, nuclei structure and stromal architecture. It demonstrates that the model can be effectively used for the evaluation of gland segmentation algorithms.
Contextual Fuzzy Ranking for Web Services Discovery in a Hybrid Architecture

The fast increasing number of web services is transforming the web from a data-oriented repository to a service-oriented repository. This repositories offers a single search point for all shared services which makes the discovery of web services is one of main action in the service-oriented architecture. After a deep study of the existing repositories authors propose a hybrid architecture for web service discovery with multi-level domain services implemented with mult-iagent technology to fulfill some of the lacks in previous works. Also, propose a contextual fuzzy ranking approach to help the user to select the best services according to his needs.
Tracking the Impacts of Covid-19 Pandemic-Related Debris on Wildlife Using Digital Platforms

Since the start of the severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2; COVID-19) pandemic in December 2019, there have been global surges of single-use plastic use. Due to the importance of personal protective equipment (PPE) and sanitation items in protecting against virus transmission, facemasks, respirators, disposable gloves, and disposable wet wipes have become global staples in households and institutions. Widespread use and insufficient infrastructure, combined with improper waste management have resulted in an emerging category of litter. With widespread presence in the environment, such items pose a direct threat to wildlife in a series of ways. We examined the scope of COVID-19 pandemic-related debris, including PPE and sanitation items, on wildlife from April 2020 to December 2021. We document the geographic occurrence of incidents, debris types, and consequences of incidents that were obtained from social media searches, unpublished reports from colleagues, and reports available from the citizen science database “Birds and Debris”. There were 114 unique sightings of wildlife interactions with pandemic-related debris (38 from 2020 and 76 from 2021). Most incidents involved birds (83.3%), with smaller numbers affecting mammals (10.5%), invertebrates (3.5%), fish (1.8%), and sea turtles (0.9%). Sightings originated in 23 countries, and consisted mostly of entanglements (42.1%) and nest incorporations (40.4%). We verified sightings by contacting the original observers and were able to identify replicated sightings to increase the resolution of the data collected. Overall, the global scope of this study demonstrates that online and social media platforms are a valuable way to collect citizen science observations to collect biologically relevant data and track rapidly emerging environmental challenges.
Estimating Skill Proficiency from Resumes

This paper attempts to solve a real-life problem of estimating proficiency levels of skills held by a person from her resume. This is a challenging problem because no other source of information than resumes is available and skill proficiency is a complex function of various aspects of a person's experience. A resume often mentions various skills held by a person and her experience in applying these skills as part of work or project experience. We extract skills and other relevant information automatically from resumes and capture skill related information in terms of a feature vector. We propose two techniques to automatically learn a skill proficiency estimation function using these feature vectors – (i) supervised neural network based technique where we introduce a novel loss function to combine label information with domain-specific constraints and (ii) weakly supervised clustering based technique. We evaluate these techniques along with two competent baselines on a dataset of Information Technology (IT) resumes focusing on 5 major skills – Java, Python, Databases, Embedded Systems, and Machine Learning.
Breast Cancer Diagnosis with the Support of Machine Learning Algorithms

Breast Cancer is a widespread problem for most of the women around the world. This cancer can be detected by identifying tumors, i.e. malignant and benign. Doctors in the field of breast cancer diagnosis required reliable diagnosing procedure for finding malignant tumors. In this field, a fast pace software tool is required to accurately find malignant tumors. In this area, a large number of researches are going on around the world. In this work, various ML Algorithms are identified and applied on the Breast cancer data, to identify appropriately the malignant tumors, and an accuracy comparison of these ML algorithms is also presented in this paper.
A Fast and Accurate Point Pattern Matching Algorithm Based on Multi-Hilbert Scans

This paper proposes a novel distance measurement using multi-Hilbert scans for matching point patterns on images. A modified Hausdorff distance has been widely used for point pattern matching, recognition tasks, and evaluation of medical image segmentation. However, the computation cost increases sharply with the number of feature points or the increase of data sets. Multi-Hilbert Scanning Distance (MHSD) based on sets of one-dimensional points using Hilbert scans is introduced to overcome this problem. MHSD consists of a combination of four directional Hilbert scans and diagonally shifted Hilbert scans. The proposed method was tested on vehicle images and compared with Hausdorff distance, partial Hausdorff distance, and modified Hausdorff distance. Experimental results show that the proposed method outperforms the compared methods.
Multiuser Optical Image Authentication Platform Based on Sparse Constraint and Polar Decomposition in Fresnel Domain

In this paper, a new multiuser optical image encryption and authentication technique is proposed. Sparse multiplexing and polar decomposition are used in the Fresnel domain to obtain the ciphertext of an input image. To enable the multiuser platform, multiple private keys are obtained through polar decomposition during the encryption process. It will allow multiple authorized user to access the secure information simultaneously without having the key distribution problem among them. The proposed method has a large key space and robust against several attacks, such as, contamination attacks (noise and occlusion), brute force attack, plaintext attacks, and special iterative attack. A comparative analysis of the presented technique is also performed with the similar existing techniques. The numerical simulation results demonstrate the robustness and feasibility of the proposed algorithm.
Personalized Recommendation System of Smart Library Based on Deep Learning

As the fastest developing technology in the field of artificial intelligence in recent years, speech recognition, natural language processing and so on. It is difficult for college readers to find books that are really interesting or valuable from a large number of books only by relying on the traditional retrieval based services. Using data mining technology and personalized recommendation technology, according to the differences of readers’ own information needs, actively recommend books that meet the needs of readers to readers. This active service method not only improves the service level of University Libraries and makes the development of university libraries more comprehensive and humanized, but also can explore the potential information needs of readers and improve the borrowing rate of books in the collection, Maximize the utilization of book resources.
Draw-n-Replace: A Novel Interaction Technique for Rapid Human-Correction of AI Semantic Segmentation

A large volume of precise semantic segmentation data is required for training AI systems such as autonomous vehicles. To combine the speed of machine segmentation and the accuracy of human labeling, researchers have explored the human-AI pipeline, where the machine proposes segmentations and the human makes corrections. Unfortunately currently, machine-segmentation is too inaccurate and time-consuming for humans to correct. As a result, the majority of precise semantic segmentation is currently done manually. For the human-AI pipeline to be viable, improvements in both AI segmentation accuracy and human-correction speed are needed. We focus on the latter and present a novel interaction technique called Draw-n-Replace for faster human-correction of machine-segmentation mistakes. We show that our technique is 2.36 times faster than the conventional approach.
Road Traffic Anomaly Detection: A Survey

Lately the number of vehicles circulating on roads had been widely increased especially in the big cities, leading instantly to congestion and consequently the accidents rate increasement. In order to reduce traffic issues and enhance road safety, so many researchers focus their vision on this era problem trying to look for solutions by developing algorithms and embedded applications based on image processing and artificial intelligence, therefore we aim to automate the traditional detecting systems used to spot traffic violations such as: speeding detection; crossing a red light; driving on a prohibited lane, etc. this offenses considered among the principal factors of road accidents, for this main reason the built of those embedded systems play a very important role for traffic accident reduction through monitoring the road safety and automating the penalty detection. In this article, we are trying to study the most recent techniques based on computer vision to monitor and detect traffic offenses, lightning the strengths and weaknesses of these techniques, additionally we provide research a comparison and study basis to facilitate their future work also improve robust approaches to detect road violations.
A computational approach to understand building floor plan images using machine learning techniques

In recent past, there is a steep increase in the use of online platforms for the search for desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this chapter, we present state of the art methods proposed in literature for automatic recognition and understanding of indoor spaces through their floor plan and RGB images and results. This chapter focuses on understanding of indoor spaces by analyzing their floor plans and generating a textual narration out of them. Floor plan image understanding is performed in a multistaged manner by recognizing visual components and generating a semistructured description for the floor plan image. Novel feature encoding is proposed for representing floor plan images for developing a machine learning based room recognition model. The quality of the generated narration is evaluated by comparing with the human written descriptions using state of the art language evaluation metrics. The proposed framework gives good quality descriptions of floor plans and has application in various applications such as, web-based real estate market, indoor navigation of robot or visual impaired person, similar plan retrieval.
Multi-feature-Based Type-2 Fuzzy Set Induced Parallel Rank-Level Fusion in Face Recognition

This paper projects a new decision-making model for multi-feature and multi-classifier fusion by integrating the type-2 fuzzy set-based approach to mitigate the factors that pretend the face recognition accuracy. A new scheme is expended in the present research to generate a type-2 fuzzy set-based ranks for each of the top-ordered classes, which are selected on the basis of weights or confidence factors, yielded by the classifier. These type-2 fuzzy sets are shaped for each of the top-ordered classes from the feature vectors of the test image and those of the training samples of the respective class. Type-reduced defuzzified value is the conceived as the fuzzy rank of the particular class. Considering the three different feature vectors and the respective outputs of the classifier, the fuzzy ranks are fused weighted by the confidence factors for each of the regarded classes to generate the fused scores. Based on these scores, the class of the test image is recognized. Well-known databases are used to evaluate the performance of the proposed model. The experimental results have manifested an improved performance than other state-of-the-arts methods despite varied occlusion, pose, and illumination present in the databases.
Cyclic Study of Graph Search Algorithm in Lower Bound of Ramsey Number

In this paper, we study the algorithm of searching the lower bound of the equal Ramsey number by constructing the circular ingenious graph. We present an efficient algorithm, which has been programmed and implemented. From this, we obtain a circular ingenious graph with 46 points (4, 7), and prove that R (4.7) ≥ 47.