Reviewerly/exHarmony huggingface dataset:
Design and fabrication of 3D printed QuadDrone for altitude measurement

The main motivation towards the application of the proposed QuadDrone is precise altitude measurement.The QuadDrone is designed using SolidWorks and fabricated using 3D printing technology.The drone body is fabricated using poly lactic acid (PLA) material (black) and acrylonitrile butadiene styrene (ABS) material (white) is used to fabricate drone landing box.For this purpose, Ultimaker and CubePro 3D printers are used.The printed parts are tested for dimensional accuracy and surface roughness.The electronic components used for drone are flight control (KK2), electronic speed controllers (ESCs), transmitter and receiver (six channels), BLDC motors (2,280 KV), propellers (5045) and pressure sensor for altitude measurement (BMP180).The QuadDrone is assembled with 3D printed parts and electronics components.The difficulties encountered while developing the drone are assembly errors, sizing issues and weight balancing.The recent technologies like 3D printing, rapid prototyping and drone technology are used to develop the QuadDrone.The performance to measure the altitude of QuadDrone is tested in the open environment and it will act as a precision altimeter.
Body Joints and Trajectory Guided 3D Deep Convolutional Descriptors for Human Activity Identification

Human Activity Identification (HAI) in videos is one of the trendiest research fields in the computer visualization. Among various HAI techniques, Joints-pooled 3D-Deep convolutional Descriptors (JDD) have achieved effective performance by learning the body joint and capturing the spatiotemporal characteristics concurrently. However, the time consumption for estimating the locale of body joints by using large-scale dataset and computational cost of skeleton estimation algorithm were high. The recognition accuracy using traditional approaches need to be improved by considering both body joints and trajectory points together. Therefore, the key goal of this work is to improve the recognition accuracy using an optical flow integrated with a two-stream bilinear model, namely Joints and Trajectory-pooled 3D-Deep convolutional Descriptors (JTDD). In this model, an optical flow/trajectory point between video frames is also extracted at the body joint positions as input to the proposed JTDD. For this reason, two-streams of Convolutional 3D network (C3D) multiplied with the bilinear product is used for extracting the features, generating the joint descriptors for video sequences and capturing the spatiotemporal features. Then, the whole network is trained end-to-end based on the two-stream bilinear C3D model to obtain the video descriptors. Further, these video descriptors are classified by linear Support Vector Machine (SVM) to recognize human activities. Based on both body joints and trajectory points, action recognition is achieved efficiently. Finally, the recognition accuracy of the JTDD model and JDD model are compared.
Query Expansion For Medical Domain To Augment The Web Search

There has been a huge upsurge in the data available on the web making it voluminous over the last few years. Most users who search the internet for the medical information have no adequate knowledge about the medical domain. Understanding queries plays a huge role in helping users navigate through the abundant data and find the required information. Query feedbacks generally drift from the topic due to the various vocabulary mismatches or due to the ambiguous synonyms. A system has been developed to bridge the gap between expert users and the laypersons. We develop a system where by users query is enhanced by adding terms provided by experts. The appropriate query term is added from a medical expert vocabulary which is selected by the classifier based on the euclidean distance and a term based methodology for deciphering the query reformulation actions. The proposed system performs well in terms of precision and recall.
Modern Multi-Document Text Summarization Techniques

Text Summarization is the technique in which the source document is simplified, valuable information is distilled and an abridged version is produced. Over the last decade, the focus has shifted from single document to multi-document summarization and despite significant progress in the domain, challenges such as sentence ordering and fluency remain. In this paper, a thorough comparison of the several multi-document text summarization techniques such as Machine Learning based, Graph based, Game-Theory based and more has been presented. This paper in its entirety condenses and interprets the numerous approaches, merits and limitations of these techniques. The Benchmark datasets of this domain and their features have also been examined. This survey aims to distinguish the various summarization algorithms based on properties that prove to be valuable in the generation of highly consistent, rational, summaries with reduced redundancy and information richness. The conclusions presented by this paper can be utilized to identify the advantages of these papers which will help future researchers in their study of this domain and ensure the provision of important data for further analysis in a more systematic and comprehensive manner. With the aid of this paper, researchers can identify the areas that present some scope for improvement and thereafter come up with novel or possibly hybrid techniques in Multi-Document Summarization.
Large-scale refactoring challenges and coordination in open source software development

Increasingly complicated software makes it difficult to attract or maintain open source software (OSS) contributors. Faced with such challenges of increasingly complicated software design, large-scale refactoring that radically restructures the architecture of the software can be one of the solutions. In this study, we investigate and illustrate how OSS contributors accomplish large-scale refactoring in OSS development in which there is no significant corporate participation. In our observations, as the costs of coordination requirements rise, OSS contributors increase coordination capability to manage dependencies among multiple sources during the large-scale refactoring periods. Our findings suggest that OSS contributors episodically use traditional coordination mechanisms during the large-scale refactoring periods. Our study provides actionable insights into how OSS contributors make joint action that cannot be achieved by individuals working independently and use the provision of rewards in order to achieve a shared, explicit goal of large-scale refactoring.
Spatial pattern-shifting method for complete two-wavelength fringe projection profilometry: erratum

We present an erratum and generalization to our Letter [ Opt. Lett. 45 , 3115 ( 2020 ) OPLEDP 0146-9592 10.1364/OL.392102 ]. This erratum corrects an error in Eq. (12), and the generalization converts <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>R</mml:mi> <mml:mi>h</mml:mi> </mml:msub> </mml:mrow> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>k</mml:mi> <mml:mi>h</mml:mi> </mml:msub> </mml:mrow> </mml:math> for more general situations of wavelengths. Neither has any influence on the conclusions of the original Letter.
Evolutionary computation for feature selection and feature construction

tutorial Evolutionary computation for feature selection and feature construction Share on Authors: Bing Xue Victoria University of Wellington, New Zealand Victoria University of Wellington, New ZealandView Profile , Mengjie Zhang Victoria University of Wellington, New Zealand Victoria University of Wellington, New ZealandView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 1283–1312https://doi.org/10.1145/3377929.3389857Online:08 July 2020Publication History 0citation111DownloadsMetricsTotal Citations0Total Downloads111Last 12 Months49Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Optimized Solution for Ranking Based On Data Complexity

Cloud Computing is an emerging field with lot of possibilities for the maintenance at the Infrastructure Layer and Software Layer. A storage architecture is associated with two processes namely the storage and the retrieval process. The storage architecture plays a vital role in how quickly the data is retrieved. The retrieved data is presented as per the weight of the retrieved data. This paper presents a novel secure storage and the ranking mechanism for the documents for cloud. As no previous reference for any data is kept at the server, the data is encrypted based on the co-relation between the data files calculated by Cosine similarity. The ranking of the retrieved data is done through Supervised Machine learning mechanism. The evaluation of the parameters are done on the base of computation time and total number of true retrievals on multi-keyword search. Multiple dataset from Kaggle are used to perform and cross validate the proposed algorithm
The Research of Online Classroom Status Feedback Based on Facial Expression Recognition Technology

In the case of the difficulty of teachers to know the learning status of students in the online classroom, this paper mainly proposes a learning status feedback method based on facial expression recognition. With the students’ images captured on class, the OpenCV facial detection is used to extract the facial information, which will be classified into seven emotion categories through the ResNetl8 convolutional neural network model. Through the PAD emotion model, the seven types of emotions are mapped to the three dimensions of the PAD emotion space, and the mapping values are used to calculate the students’ learning engagement with the formula, so that it can numerically quantify the abstract learning emotion status. Based on statistical principles, the learning engagement is refined to three characteristics - level, volatility, and persistence. Taking an online physics class divided into different teaching sessions as an example, this paper analyzes the study status of students with PAD model and provides feedback, which confirms the feasibility of using facial emotion recognition technology in online classroom status. In this way, teachers can easily judge students’ learning engagement in online classroom and adjust their teaching strategies to improve the quality of online classroom teaching.
Smart Python Coding through Voice Recognition

In today’s world, many artificial intelligence applications developed using programming languages like Python, R and so on. Each language comes with its own programming structure and syntactical forms. Programmers are broadly classified into three categories namely, novice users, knowledge intermittent and expert one. For novice users, it is always a challenge to write a code without typographic errors though users know theoretical knowledge of Programming language, its structure and syntax as well as logic of program. Therefore, this paper explores use of voice recognition technique in the field of programming, specifically for writing program with Python programming language. In experimental analysis, it found helpful for new Python programmers and provide new learning curve for programmers wherein beginner can experience hassle free program writing. This paper adds new way of creating interest in beginners for judging their coding paradigm understanding and explore one of the area for user experience field for better programming Integrated Development Environment Development (IDE).
Cross Breed Clustering Algorithm for High Dimensional Data

Clustering plays a major role in machine learning and also in data mining. Deep learning is fast growing domain in present world. Improving the quality of the clustering results by adopting the deep learning algorithms. Many clustering algorithm process various datasets to get the better results. But for the high dimensional data clustering is still an issue to process and get the quality clustering results with the existing clustering algorithms. In this paper, the cross breed clustering algorithm for high dimensional data is utilized. Various datasets are used to get the results.
HighPU: a high privacy-utility approach to mining frequent itemset with differential privacy

In the field of data mining, frequent itemset mining (FIM) is a popular technique for analysing transaction datasets and establishing the foundation of association rules. Publishing frequent itemsets, however presents privacy challenges. Differential privacy provides strong privacy assurance to users. In this paper, we study the problem of mining frequent itemsets under the rigorous differential privacy model. We propose an approach, called HighPU, which achieves both high data utility and high degree of privacy in FIM. HighPU begins by truncating transactions over the original dataset. Then HighPU directly searches for maximal frequent itemsets. And we use a consistent approach to improve the accuracy of the results. Extensive experiments using several real datasets illustrate that HighPU significantly outperforms the current state of the art.
INCREASING THE EFFECTIVENESS OF THE CYBERSECURITY TEACHING AND LEARNING BY APPLYING ACTIVITY THEORY AND NARRATIVE RESEARCH

Several studies indicate that companies are still facing with severe challenges in recruiting specialists in cybersecurity, for protecting the companies' systems from cybersecurity attacks.These challenges are caused by the existing gap between knowledge, practical experience, and critical soft skills as developed by the cybersecurity educational programs and trainings and the real needs of the companies.The increase of the effectiveness of teaching and training in developing the students' skills as required by the companies becomes more and more important.The paper aims to present the challenges of applying the Activity Theory and the narrative research in improving the effectiveness of teaching and learning processes in a master degree program on Cybersecurity, delivered by the university where two of the co-authors are working.
Intelligent Neighborhood Indexing Sequence Model for Healthcare Data Encoding

Recently, information security in the healthcare sector has become essential to ensure confidentiality in medical data. At the same time, automated disease diagnosis using deep learning (DL) models also gained considerable attention to accomplish enhanced classification performance. This paper designs an intelligent neighborhood indexing sequence based on encoding with a classification model for healthcare information security (INISEC-HIS). The proposed INISEC-HIS technique aims to accomplish security in medical data transmission and diagnosis. The neighborhood indexing sequence (NIS) technique is applied to securely transmit the data, which transforms the medical data into an encoded format. Besides, a novel artificial fish swarm algorithm (AFSA) with deep neural networks (DNN) model is used for the classification process. The design of AFSA to optimally adjust the hyperparameters of the DNN model shows the study's novelty. An extensive simulation analysis takes place to examine the improved outcomes of the INISEC-HIS technique, and the obtained results highlighted the supremacy over the other techniques.
Feature Extraction through Sentiment Analysis of Tourist Sentiments using Deep Learning Techniques like CNN, RNN and LSTM

Sentiments are the emotions which are communicated among individuals. These are opinions given by people on any item, product or service availed or experience online. This paper discusses that part of research area which involves the analysis of sentiments exchanged by people online that further tells how sentiments and features through online tourist reviews are extracted using deep learning techniques. Tourist behavior can be judged by tourists reviews for various tourist places, hotels and other services provided by tourism industry. The proposed idea of the paper is to show the high efficiency of deep learning techniques like CNN, RNN,LSTM to extract the features online by use of extra hidden layers. Further, comparison of these techniques as well as comparison of these techniques with machine learning classical algorithms like SVM, Naïve Bayes, KNN,RF etc has been done to show that deep learning methods are more efficient than classical machine learning algorithms. The accurate capturing of attitudes of tourists towards tourist places, hotels &amp; other services of tourism industry plays utmost important role to enhance the business model of tourism industry. This can be done through sentiment analysis using deep learning methods efficiently. Classification of polarity will be done by extracting textual features using CNN,RNN,LSTM deep learning algorithms. Extracting features are fed to deep learning classifier to classify the review into either positive, negative or neutral type of reviews. After comparing various deep learning and classical techniques of machine learning, it has been concluded that LSTM,RNN give best results to classify reviews into positive and negative reviews rather than SVM,KNN classical techniques. In this way sentiment analysis has been done and the proposed idea of this research paper is change in the machine learning techniques or methods from classical algorithms to neural network deep learning methods which in future definitely will give better results to analyze deeply the sentiments of tourists to find out the liking and disliking of various tourist places, hotels and related tourism services that will help tourism business industry to work on the gap in existing services provided by them and system can become more efficient in future. Such improved tourism system will give benefits to tourists or users in terms of better services and undoubtedly it will help tourism industry to enhance business in future.
An effective preprocessing algorithm for model building in collaborative filtering-based recommender system

Recommender systems suggest interesting items for online users based on the ratings expressed by them for the other items maintained globally as the rating matrix. The rating matrix is often sparse and very huge due to large number of users expressing their ratings only for a few items among the large number of alternatives. Sparsity and scalability are the challenging issues to achieve accurate predictions in recommender systems. This paper focuses on model building approach to collaborative filtering-based recommender systems using low rank matrix approximation algorithms for achieving scalability and accuracy while dealing with sparse rating matrices. A novel preprocessing methodology is proposed to counter data sparsity problem by transforming the sparse rating matrix denser before extracting latent factors to appropriately characterise the users and items in low dimensional space. The quality of predictions made either directly or indirectly through user clustering were investigated and found to be competitive with the existing collaborative filtering methods in terms of reduced MAE and increased NDCG values on bench mark datasets.
Semantics-based key concepts identification for documents indexing and retrieval on the web

Bridging the semantic gap on the web remains one of the crucial challenges for current horizontal and domain-specific information retrieval systems. This challenge becomes even more pronounced when users express their information needs using short queries that are formulated using a few number of keywords, therefore relying on keywords for indexing web documents results in degrading the quality of the returned results. In this article, we introduce an approach that employs knowledge captured by large-scale knowledge resources to identify key query terms and retrieve semantically-relevant documents. In this context, key terms are mapped to their semantic correspondences and variable term weights are assigned based on the semantic and taxonomic relations for each term. To demonstrate the effectiveness of the proposed approach, we have conducted experimental evaluation using Glasgow's NPL test collections. Findings indicate that the effectiveness has improved against four conventional similarity metrics that are based on the bag-of-words similarity model.
Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias

Lexical disambiguation is a major challenge for machine translation systems, especially if some senses of a word are trained less often than others.Identifying patterns of overgeneralization requires evaluation methods that are both reliable and scalable.We propose contrastive conditioning as a reference-free blackbox method for detecting disambiguation errors.Specifically, we score the quality of a translation by conditioning on variants of the source that provide contrastive disambiguation cues.After validating our method, we apply it in a case study to perform a targeted evaluation of sequence-level knowledge distillation.By probing word sense disambiguation and translation of gendered occupation names, we show that distillation-trained models tend to overgeneralize more than other models with a comparable BLEU score.Contrastive conditioning thus highlights a side effect of distillation that is not fully captured by standard evaluation metrics.Code and data to reproduce our findings are publicly available. 1
A Privacy-Preserving Protocol for Verifiable File Search on the Cloud

Because cloud computing is increasingly popular, a great deal of documentation is outsourced to the cloud with decreased leadership costs and easy access. While encryption helps prevent user data confidentiality, it creates a difficult issue with well-functioning yet virtually efficient safe search features over encrypted information. It should always be encrypted before cloud outsourcing to preserve the privacy of personal documents that are stored in the cloud environment. Retrieving the same data on the cloud is also a still, tedious task. To obtain information, there are several methods available in which keyword-enabled data entry is one of the best methods. Most of these methods are restricted to the handling of a single keyword query. In order to improve search effectiveness and speed, a multi-key word-search method can be used to obtain a respective cloud.
Text Classification Using Ensemble Of Non-Linear Support Vector Machines

With the advent of digital era, billions of the documents generate every day that need to be managed, processed and classified. Enormous size of text data is available on world wide web and other sources. As a first step of managing this mammoth data is the classification of available documents in right categories. Supervised machine learning approaches try to solve the problem of document classification but working on large data sets of heterogeneous classes is a big challenge. Automatic tagging and classification of the text document is a useful task due to its many potential applications such as classifying emails into spam or non-spam categories, news articles into political, entertainment, stock market, sports news, etc. The paper proposes a novel approach for classifying the text into known classes using an ensemble of refined Support Vector Machines. The advantage of proposed technique is that it can considerably reduce the size of the training data by adopting dimensionality reduction as pre-training step. The proposed technique has been used on three bench-marked data sets namely CMU Dataset, 20 Newsgroups Dataset, and Classic Dataset. Experimental results show that proposed approach is more accurate and efficient as compared to other state-of-the-art methods.