Reviewerly/exHarmony huggingface dataset:
Diphthongs in Igbo? Language Contact in the Igbo and Anaang Borderlands

This study examined Anaang substratum influence on Igbo, and how it relates to socio-cultural biographies (such as trade, intermarriage and small-scale migration) amongst Igbo and Anaang speakers in the borderlands. Drawing on ethnographic observation, semi-structured and sound-elicitation interviews with 20 informants made up of Igbo and Anaang speakers, the study identified Anaang substratum influence on Igbo involving the transfer of “vowel-glide” sequences in Anaang to Igbo, which we analysed as “imposition,” and which may build up to the process of diphthongisation in the Igbo language. There are signs of incipient structural diffusion or convergence as revealed in the adoption of the Anaang “glide-vowel” phonotactic feature (for example, occurrence in medial and final positions and co-occurring with plosives and fricatives) in the version of Igbo spoken in the borderlands, as well as occurrence of some “glide-vowel” sequences in initial positions (which goes against Anaang phonotactics) in the version of Anaang spoken in the borderlands. It is concluded that complex linguistic outcomes in the borderlands reveal the typological distance between languages in contact.
Graph matrix completion for power product recommendation

The recommendation system has achieved great success for decades, some are still not solved well, especially in data sparseness. Graph convolution network(GCN) can be well applied to recommender systems, but the previous methods only consider vector representations of users and items. The latent in user-item interactions is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect. we propose a new Graph matrix completion(GMC) framework, which add user-item interaction information and side information to the graph auto encoder network, and capture differentiable information transmission on bipartite interaction graph. Multi-layer decoder is adopted to optimize loss for better map the potential representation of user/item. Our model is evaluated on multiple benchmark datasets and outperforms the state-of-the-art algorithm.
Automatic Categorized Corpus Creation of Hindi Poetries Based on Rasa(s) for Linguistics Research

Corpus creation is required for solving natural language processing problems and linguistic research using machine learning and deep learning. Classification problems solved using supervised learning methods need labeled corpus. It is tedious task if done manually. This research article is creating labeled corpus of Hindi poetries based on Rasa(s). Preprocessing of the extracted documents is done using NLP techniques to create a clean corpus ready to use for solving classification problem or any other problem. The garbage elements are removed from the extracted files, and file containing excess garbage is deleted. The corpus created comprises of poetries of 9 Rasa(s) also called Navrasa. A total of 1089 files were extracted comprising of 333 Adbhuta, 5 Bhayank, 25 Hasya, 70 Karuna, 6 Raudra, 277 Shanta, 218 Shringar, 147 Veera, and 8 Vibhasta Rasa poetries in 04:20:23 hrs with negligible human intervention. A total of 17 garbage files were deleted, and the final corpus have 1072 poetry documents comprising of 328 Adbhuta, 5 Bhayank, 25 Hasya, 68 Karuna, 6 Raudra, 272 Shanta, 216 Shringar, 144 Veera, and 8 Vibhasta Rasa poetries.
Intelligent recognition technology of heavy metal pollution based on deep learning and fuzzy clustering and its application

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsIntelligent recognition technology of heavy metal pollution based on deep learning and fuzzy clustering and its applicationAuthors: Yuhan XuYongtao WangYuhan XuState Key Laboratory of Marine Geology, Tongji UniversitySearch for more papers by this author and Yongtao WangBGP China National Petroleum CorporationSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3594589.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn the process of anomaly identification of polluted soil, in order to improve the analysis and interpretation ability of inversion results of multi-electrode resistivity method, and overcome a series of shortcomings of manual interpretation, such as unable to guarantee the interpretation quality, this paper combines deep learning, fuzzy c-means clustering and hypothesis testing technology to form a set of intelligent detection and identification technology suitable for various types of pollution. From the experimental results of the numerical model and the processing results of the measured data, it can be known that this method can complete the intelligent identification of contaminated soil effectively.Keywords: electrical/resistivity, machine learning, environmentalPermalink: https://doi.org/10.1190/segam2021-3594589.1FiguresReferencesRelatedDetails 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 Yuhan Xu and Yongtao Wang, (2021), "Intelligent recognition technology of heavy metal pollution based on deep learning and fuzzy clustering and its application," SEG Technical Program Expanded Abstracts : 1956-1960. https://doi.org/10.1190/segam2021-3594589.1 Plain-Language Summary Keywordselectrical/resistivitymachine learningenvironmentalPDF DownloadLoading ...
COVID-19 and the Digital Identity of Faculty

Based on Bennett's theoretical framework, “The Digital Practitioner,” rooted in Maslow's Hierarchy of Need, this mixed-method study investigated the digital identity of the Pakistani universities' faculty in the COVID-19 context. The data revealed that the faculty is willing to adopt digital identity with modesty, empathy, and positivity while the negative feelings like fear, risk, and mistakes have been accepted with optimism. The implications of the study guide the policymakers in academia to reflect on the teachers' digital identity and address their fears and challenges through institutional support and proper professional development opportunities.
The Roles of Small and Medium-Sized Enterprises in Blockchain Adoption

Despite the growing maturity of Blockchain technology and an increasing deployment in Supply Chain and Logistics, many small and medium-sized enterprises (SMEs) struggle to use the technology for their benefit. Based on 27 expert interviews, we develop a typology of Blockchain adoption approaches for SMEs and discuss their implications. We find that SMEs can approach the technology as either an Observer, a Cooperator, or a Service Provider based on their technological expertise, the expected relevance of the technology for their organization, and their market power.
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning

Classification has been a major task for building intelligent systems as it enables decision-making under uncertainty. Classifier design aims at building models from training data for representing feature-label distributions--either explicitly or implicitly. In many scientific or clinical settings, training data are typically limited, which makes designing accurate classifiers and evaluating their classification error extremely challenging. While transfer learning (TL) can alleviate this issue by incorporating data from relevant source domains to improve learning in a different target domain, it has received little attention for performance assessment, notably in error estimation. In this paper, we fill this gap by investigating knowledge transferability in the context of classification error estimation within a Bayesian paradigm. We introduce a novel class of Bayesian minimum mean-square error (MMSE) estimators for optimal Bayesian transfer learning (OBTL), which enables rigorous evaluation of classification error under uncertainty in a small-sample setting. Using Monte Carlo importance sampling, we employ the proposed estimator to evaluate the classification accuracy of a broad family of classifiers that span diverse learning capabilities. Experimental results based on both synthetic data as well as real-world RNA sequencing (RNA-seq) data show that our proposed OBTL error estimation scheme clearly outperforms standard error estimators, especially in a small-sample setting, by tapping into the data from other relevant domains.
Research on Feature Extraction and Tendency Analysis of Chinese Language Sentimental Expression in Network Context

The current network language only considers the expression of its basic meaning, and lacks in-depth analysis of sentimental characteristics and tendencies. For this reason, this paper investigates the sentimental analysis methods of network language based on the Chinese text information of network social media platform. Through the training of the existing data sets, we complete the data cleaning and feature word extraction. Then, a multi-dimensional fine-grained sentiment classification algorithm is designed by using neural network to reduce the dimension of feature vector. Finally, the structured linguistic features are used to optimize the sentiment score step by step, and the text sentiment classification is implemented, and a tendency analysis model considering the polarity of sentiment is further established. The validity of the model is verified by the real data collected in the network. The results show that the method has a significant advantage in Chinese data sets, and the recognition rate is higher than that of similar sentiment analysis methods.
Task Scheduling Algorithm of Cyber-Physical System Base on Complex Industry and Big Data

In order to address the problem of unbalanced load of cyber-physical systems caused by high data concurrency, heterogeneous resources, and complex task nature in complex industrial and big data environments, this paper proposes a dynamic feedback task scheduling algorithm based on queuing theory (Queuing Theory Task Scheduling). The algorithm firstly uses the cluster monitoring system to collect data on each server and waiting queue in the cluster. Then, the monitoring system calculates various performance indicators of the system based on the collected data to judge the status of the servers. Finally, the corresponding scheduling strategy will be executed. To verify the effectiveness of queuing theory task scheduling (QTTS), this paper uses SimEvents toolkit to build an experimental environment and conduct simulation experiments about the total time to complete the tasks and average waiting time of the tasks. The experimental results show that compared with the traditional scheduling algorithms RR and Min-Min, QTTS can effectively reduce the total time to complete the tasks and average waiting time of the tasks.
CLARK: A Design Science Research Project for Building and Sharing High-Quality Cybersecurity Curricula

The Cybersecurity Labs and Resource Knowledge-base cybersecurity curriculum resource library is a large-scale design science research project developed to address critical demands in teaching cybersecurity and help faculty prepare cybersecurity graduates for the workforce. This article includes a description of the CLARK curriculum model, system, and collections, our experiences in its evolution, and the lessons learned that might benefit similar DSR projects to rapidly disseminate high-value curricular resources to the education community.
Research on Users’ Interactive Experience of Mobile Music Apps Based on Eye Tracking Test

Based on the measured data of eye movement, this article shifts the focus of eye movement research in interactive experience from static objects to dynamic process analysis and collects several key data on eye movement during the interaction between users and mobile devices. Therefore, this paper studies the usability of app son mobile devices based on eye tracking technology by selecting appropriate indexes of emotional user experience and eye movements. It also analyzes the homogeneity of variance with emotion as a factor and the cognitive statistics of different emotional states. In the end, the paper summarizes the relationship between level s of interaction difficulty and eye movement as well as its impact on the emotional user experience.
Geometric Properties of Evolutionary Graph States and Their Detection on a Quantum Computer

Geometric properties of evolutionary graph states of spin systems generated by the operator of evolution with Ising Hamiltonian are examined, using their relationship with fluctuations of energy. We find that the geometric characteristics of the graph states depend on properties of the corresponding graphs. Namely, it is obtained that the fluctuations of energy in graph states and therefore the velocity of quantum evolution, the curvature, and the torsion of the states are related to the total number of edges, triangles, and squares in the corresponding graphs. The obtained results give a possibility to quantify the number of edges, triangles, and squares in a graph on a quantum devise and achieve quantum supremacy in solving this problem with the development of a multi-qubit quantum computer. Geometric characteristics of graph states corresponding to a chain, a triangle, and a square are detected on the basis of calculations on IBM's quantum computer $\textrm{ibmq\_manila}$.
Membangun Aplikasi Cazh Support Berbasis Android Menggunakan Framework React Native

Cazh Support is an android application intended for couriers owned by merchant who use Cazh POS. If a buyer purchases goods through Cazh Commerce using the COD feature and is sent via courier, the merchant will receive a notification via the Cazh POS application. Before the Cazh Support application existed, merchants informed the courier via manual method or via WhatsApp chat, so that the workflow was less effective and efficient. For this reason, the Cazh Support application was made to support the performance of the courier in receiving work and completing work. The main function of this application is to facilitate communication between the seller and the courier in terms of new jobs, showing the buyer's address to the courier, the seller can find out information that the order has been completed and the courier can view the history and statistics of the work that has been done.
19.1: <i>Invited Paper:</i> Impact of Human Fingertip Touch/Swipe/Zoom on Display

SID Symposium Digest of Technical PapersVolume 52, Issue S2 p. 254-256 Technical Sessions: Session 19: Display Measurement 1 (Display Measurement) 19.1: Invited Paper: Impact of Human Fingertip Touch/Swipe/Zoom on Display Prof. Dr. Wolfgang P. Weinhold, Prof. Dr. Wolfgang P. Weinhold Institute for Surface and Product Analysis - ISPA, Würzburg, GermanySearch for more papers by this authorDr. Helen Wang, Dr. Helen Wang Institute for Surface and Product Analysis - ISPA, Würzburg, GermanySearch for more papers by this author Prof. Dr. Wolfgang P. Weinhold, Prof. Dr. Wolfgang P. Weinhold Institute for Surface and Product Analysis - ISPA, Würzburg, GermanySearch for more papers by this authorDr. Helen Wang, Dr. Helen Wang Institute for Surface and Product Analysis - ISPA, Würzburg, GermanySearch for more papers by this author First published: 26 August 2021 https://doi.org/10.1002/sdtp.15084AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume52, IssueS2International Conference on Display Technology (ICDT 2021)August 2021Pages 254-256 RelatedInformation
3DSRASG: 3D Scene Retrieval and Augmentation Using Semantic Graphs

Computer Vision, encompassing 3D Vision and 3D scene Reconstruction, is a field of importance to real-world problems involving 3D views of scenes. The goal of the proposed system is to retrieve 3D scenes from the database, and further augment the scenes in an iterative manner based on the user’s commands to finally produce the required output 3D scenes, in the form of a suggestive interface. The process is done recursively to facilitate additions and deletions, until the desired scene is generated. In addition to synthesizing the required 3D indoor scenes from text, a speech recognition system has been integrated with the system that will enable the users to choose from either modes of input. The application includes the projection and rendering of 3D scenes which will enable a 360-\({^\circ }\) view of the scene. The robustness of scene generation and quick retrieval of scenes will promote the usage of this work in avenues such as story telling, interior designing, and as a helpful educational tool for autistic children.
Ethereum Blockchain based Smart Contract for Secured Transactions between Founders/Entrepreneurs and Contributors under Start-up Projects

Many implementation of blockchain technology are widely available today. This Project explains how blockchain technology improves efficiency and builds faith in funding process of startup process which affect today’s business and industries. Designing Block chain based a decentralized, distributed ledger, which records transactions or events of funding process in Start-up is discussed.Start –ups facing an issue of raising a required fund. Although there are many sources are available to entrepreneurs who wish to begin new businesses or expand existing ones, like family, friends, friends of friends, bank loan, use of internet, online crowd funding platform and many more. However, to look at the proper distribution and utilization of money and to keep track of it is main problem. This proposed system may provide the solution with blockchain technology for issues related to crowdfunding contract. Through the notion of smart contract automated interaction between and existing transaction system is discussed. A solution for the issues like security, abuse of investor and illegal transactions in crowdfunding process is prompted in this project.The idea behind model is to use ethereum based smart contract for securely and effectively handling connection between fundraisers,vendors and project manager/idea person. Blockchain enabled,distributed platforms is used to avoid fraud and to view proper utilization and distribution of money raised by different contributors.
Design of a STEM Education Product "AMIGO"

The researchers aim to respond to the policy of developing smart education put forward in the Plan of New- generation Artificial Intelligence Development and accelerate the popularization and development of Science Technology Engineering Mathematics (STEM) education in primary and secondary schools. By extracting the needs of STEM courses in smart classrooms, the researchers finally designed "AMIGO", a set of modular and emotional smart robot based on tangible interaction, which is composed of a dozen types of sensors, electronic components and a main board, with an integration of online programming courses, enabling it to be used for multiplayer competition and cooperation.
Class Aware Auto Encoders for Better Feature Extraction

In this work, a modified operation of Auto Encoder has been proposed to generate better features from the input data. General autoencoders work unsupervised and learn features using the input data as a reference for output. In our method of training autoencoders, we include the class labels into the reference data so as to gear the learning of the autoencoder towards the reference data as well as the specific class it belongs to. This ensures that the features learned are representations of individual data points as well as the corresponding class. The efficacy of our method is measured by comparing the accuracy of classifiers trained on features extracted by our models from the MNIST dataset, the CIFAR-10 dataset, and the UTKFace dataset. Features extracted by our brand of autoencoders enable classifiers to obtain higher accuracy in comparison to the same classifiers trained on features extracted by traditional autoen-coders.
Segmentation of turbulent computational fluid dynamics simulations with unsupervised ensemble learning

Computer vision and machine learning tools offer an exciting new way for automatically analyzing and categorizing information from complex computer simulations. Here we design an ensemble machine learning framework that can independently and robustly categorize and dissect simulation data output contents of turbulent flow patterns into distinct structure catalogs. The segmentation is performed using an unsupervised clustering algorithm, which segments physical structures by grouping together similar pixels in simulation images. The accuracy and robustness of the resulting segment region boundaries are enhanced by combining information from multiple simultaneously-evaluated clustering operations. The stacking of object segmentation evaluations is performed using image mask combination operations. This statistically-combined ensemble (SCE) of different cluster masks allows us to construct cluster reliability metrics for each pixel and for the associated segments without any prior user input. By comparing the similarity of different cluster occurrences in the ensemble, we can also assess the optimal number of clusters needed to describe the data. Furthermore, by relying on ensemble-averaged spatial segment region boundaries, the SCE method enables reconstruction of more accurate and robust region of interest (ROI) boundaries for the different image data clusters. We apply the SCE algorithm to 2-dimensional simulation data snapshots of magnetically-dominated fully-kinetic turbulent plasma flows where accurate ROI boundaries are needed for geometrical measurements of intermittent flow structures known as current sheets.
A Comparative Study on Classification Algorithms Using Different Feature Extraction And Vectorization Techniques For Text

We live in a world where information has a great value and the amount of information available in the text document has risen so that identifying those that are important to us becomes an issue. Because of this data, divided into categories, the user is able to navigate to the information he wants to obtain. Texts are most of the data and here text classification comes to the scene.The aim of this paper is to classify the documents automatically into their classes by comparing different feature extraction and Vectorization techniques. Classification of document requires machine learning (ML) techniques. The ML techniques that we have employed to classify the documents are Support Vector Machine (SVM), Naive Bayes (NB). The various feature extraction techniques that we have implemented are Stemming and Lemmatization and we note how the algorithms differ in performance when implemented each of the feature extraction technique and vectorization approaches. We used two vectorization techniques, such as vectorization of count vector and vectorization of term frequency inverse document frequency (TF-IDF). The results prove that according to the type of content and metric, the performance of the feature extraction and vectorization methods are contrasting; in some cases are better than the others, and in other cases is the inverse..