Reviewerly/exHarmony huggingface dataset:
A Quantum Algorithm for String Matching

Algorithms that search for a pattern within a larger data-set appear ubiquitously in text and image processing. Here, we present an explicit, circuit-level implementation of a quantum pattern-matching algorithm that matches a search string (pattern) of length M inside a longer text of length N. Our algorithm has a time complexity of $$\tilde{O}(\sqrt{N})$$ , while the space complexity remains modest at O(N + M). We report the quantum gate counts relevant for both pre-fault-tolerant and fault-tolerant regimes.
Solar Energy Foecasting Using Modified Polynomial Neural Network

In this paper, solar energy forecasting using Polynomial Neural Network (PNN) has been presented. The PNN is a flexible neural architecture whose structure is developed through different learning process. In PNN, the number of layers are not fixed. Node of the PNN are highly flexible and each node mapping among each other by realizing some polynomial type of equations such as linear, quadratic, and cubic type. Accurate forecasting of solar energy causes an efficient operations of the solar based power plants. An accurate forecasting can only make the solar energy a prominent alternative of conventional fossil fuel based power sources. In this study, the accuracy of forecasting using PNN has been observed by dividing the entire observation into training and test data. Two sets of data are used to validate the model accuracy.
An Efficient DS-LSTMM Ontology for Paddy - Agricultural Data Processing

Mining of unstructured and textual data is challenging task due to the complexity of data. Even unstructured data fails to provide clear information about data required. In the field of agriculture, data availability is vast in range which increases the complexity. To overcome those challenges, ontology concept is derived for data mining to process data. This paper focused on gathering relevant information about Paddy data for ease of processing. To extract data this paper developed a Domain-Specific Long-Short Term Memory Model (DS-LSTMM) is proposed for agricultural data processing. The proposed DS-LSTMM uses deep learning technique for data extraction. Initially, the proposed DS-LSTMM uses the construction of domain-specific data in hierarchical order. Based on the domain-specific order, agricultural data are categorized. Finally, using deep learning technique paddy data is extracted from constructed hierarchical structure. The performance of proposed DS-LSTMM is comparatively examined with search using keyword-based and Ontology-based search. The results imply that propose DS-LSTMM exhibits improved performance.
GCE: Global Contextual Information for Knowledge Graph Embedding

Most existing large-scale knowledge graphs are suffering from incompleteness, and many research efforts have been devoted to the task of knowledge graph completion. One popular approach is to learn low-dimensional representations for all entities and relations, and then employ them to infer new facts. However, we find that most of the current knowledge graph embedding models are lack of suitable strategy to utilize global contextual information. In this paper, we propose an embedding model, named GCE, to explore the capability of global contextual information to the task of knowledge graph completion. In GCE, we carefully design a global contextual information module with the attention mechanism. This module could aggregate global contextual information adaptively, thus enhancing feature representation for knowledge graph completion. To demonstrate the effectiveness of our proposed GCE, we conduct extensive experiments on two benchmark datasets FB15k-237 and WN18RR. Experimental results show that GCE achieves competitive results compared with the existing state-of-the-art embedding models on both datasets. The results validate our central hypothesis – that global contextual information is beneficial to knowledge graph completion performance.
Review of Signboard Transliteration Using Deep Learning

Extracting text from the signboard, recognizing them, and thereafter translating them are a challenging task to be done and are receiving a significant amount of attention in today's world. This fulfills applications like safe driving path planning, robot navigation, etc. We put forward and discuss the generic framework that includes deep convolutional network architectures for the purpose of signboard text detection and recognition. Systems equipped with deep learning models are known for building a scalable pipeline, since these systems are data oriented. We analyzed the sequence-to-sequence models and LSTMs to find an efficient technique, for the extensible pipeline of transliteration, by contrasting the results.
Leveraging a weakly adversarial paradigm for joint learning of disparity and confidence estimation

Deep architectures represent the state-of-the-art for perceiving depth from stereo images. Although these methods are highly accurate, it is crucial to effectively detect any outlier through confidence measures since a wrong perception of even small portions of the sensed scene might lead to catastrophic consequences, for instance, in autonomous driving. Purposely, state-of-the-art confidence estimation methods rely on deep-networks as well. In this paper, arguing that these tasks are two sides of the same coin, we propose a novel paradigm for their joint training. Specifically, inspired by the successful deployment of GANs in other fields, we design two deep architectures: a generator for disparity estimation and a discriminator for distinguishing correct assignments from outliers. The two networks are jointly trained in a new peculiar weakly adversarial manner pushing the former to fix the errors detected by the discriminator while keeping the correct prediction unchanged. Experimental results on standard stereo datasets prove that such joint training paradigm is beneficial. Moreover, an additional outcome of our proposal is the ability to detect outliers with better accuracy compared to the state-of-the-art.
Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

As a key component of a split-type intelligent sports wheelchair for the disabled, the reliability of the motor is related to the personal safety of the wheelchair user and the accurate realization of the wheelchair’s sports functions. This motor is actually just a rotating machine. In order to achieve detection and analysis of rotating machinery bearing vibration signal, a method based on wavelet and energy feature of rotating machinery fault diagnosis is introduced. This method applies wavelet to obtain de-noising and then uses wavelet packet energy feature extraction to diagnose faults effectively caused by rotating machinery such as rotor unbalance fault, rotor misalignment fault, and rotor-to-stator rub fault. Test results illustrate that when different faults occur to the bearing, it is viable to utilize pattern recognition to diagnose faults for the reason that discrepancies appear in sub-hand energy after wavelet packet decomposition. The main research conclusions of this paper are also directly applied to the fault diagnosis of such wheelchair motors.
Breast Cancer Detection and Classification Employing Convolutional Neural Networks and Support Vector Machine for Histopathological Images

In recent years, the main causes of cancer deaths that are most concerning are breast cancer. Convolution neural networks (CNN) approach is deep learning that is used to overcome many problems faced on a few featured based approaches. The CNN approach methodology explains the classification of an eosin-stained breast biopsy and also hematoxylin (H&E). The extracted features by CNN could also be utilized for the training of a support vector machine classifier. The approach consists of firstly identifying the data sources where few datasets are considered of different patients and identifying the problem. Furthermore an exploratory data analysis consists of few visualization techniques that are performed using python libraries. Finally pre-processing the data here, the feature extraction is carried out. A predictive model is constructed using support vector machine (SVM) for the classification of breast tumors. The proposed system predicts if the tumor is malignant or benign and its subclasses.
Several Considerations about Quantum Mechanics

This article has demonstrated the possibility of deriving an equivalent mathematical velocity expression while setting aside the concepts of displacement and time.The proposed velocity formula, better aligned with reality, allows a better understanding of the equivalence of matter and energy, which are comprised of the same type of basic particles.Why, then, is there a significant difference between matter and energy?This is because the combination of the basic particles is not the same.Basic particles are the basic unit of mass and energy, meaning mass and energy conservations are essentially the conservation of these basic particles.Electrons, photons, neutrons, protons, neutrinos, and other microscopic particles also consist of these basic particles.The basic particles are also the basic foundation of force: a basic particle force is the smallest force in the universe, implying that force is not continuous, but a basic particle force is the smallest unit of force.The total mass of a moving body increases with increasing velocities, and this added mass is composed of the basic particles provided by an external system.These basic particles are the foundation of the universe, and determine that physical concepts are vectors or scalars.Velocity, a vector, is the ratio between the basic particles.The concept of time is essentially academic.Although relativity equations may satisfy mathematical principles, they may represent a mathematical model with no physical meaning, not demonstrating objective physical facts.
Information and Communication Technology (ICT) knowledge of biology teachers in senior high school based on teaching experience

Abstract The percentage of schools that have network and internet access in Indonesia has reached 77.89%. While the ability of teachers to use ICT 14.43%. This is supported by the qualifications of teacher knowledge about ICT for senior high school by 14,43%. This study aims to determine the level of ICT knowledge of biology teachers and determine the relationship of ICT knowledge with teaching experience. Teacher knowledge is measured using a multiple choice test instrument with digestive system material. The research respondents consisted of 23 biology teachers in senior high school. The validity and reliability test of the instrument was carried out using SPSS with a significance of correlation &lt;0.423 and Cronbach’s Alpha 0.92 (very reliable). Data analysis of the ICT knowledge level of teacher using Microsoft Excel and to see the relationship of ICT knowledge with teaching experience using the One Way ANOVA test. The results showed the level of ICT knowledge of biology teacher from all three aspects was good with an average of 73.8. There is a significant difference between the level of ICT knowledge of the teacher and teaching experience of the teacher, sig 0.000 (p&lt;0.05). The long teacher teaching experience does not provide good teacher ICT knowledge.
LibGuides Home: Connecticut State Library Resources During Covid-19: Covid 19 Vaccine

A guide for information and resources pertaining to Covid-19 primarily from the state library.
Multimodal Interaction: Taxonomy, Exchange Formats

Today multimodal approach is relevant in any sphere of our digital life. The technologies aim to suggest the best and the most comfortable way for human-human and human-machine interaction. One of the direction to do that is the usage of multimodal communication. The new possibilities in technology (powerful mobile devices, advanced sensors, new ways of output, etc.) have opened up a new research field of multimodal interaction. In this paper, we define the detailed multimodal taxonomy and consider the exchange format for voice and facial data.
Automatic Video Editor for Reportages Assisted by Unsupervised Machine Learning

In this paper we explain the methodology for making a computer program that will automatically edit videos with the only input being files containing raw video interviews. Program produces two files as its outputs – an MP4 video file and an XML file compatible with the most popular video editing programs. MP4 video file will be the edited video program made and the XML file will contain the timeline information which the program puts together with clip durations, in and out points on the timeline, and information about the timeline itself. Such a program is useful for two general types of users. The first group would be video editors for whom it would save time - upon generating the XML they could continue improving the cut, without wasting time manually cutting and sorting clips. The second type of users that could benefit using this program are people who are not professional video editors and would use this program as a simple tool that enables them to quickly edit the interviews they shot and publish them on the web. In processing the inputted video files, among other technologies, program uses unsupervised machine learning. The program is written in Python and uses FFmpeg for converting audio and video files. The program has been tested on a limited sample of video interviews carried out in English .
The Influence of Web-Centric Course in Learning Basic Mathematics toward the Digital Literacy Skills of Preservice Mathematics Teachers

Abstract This study aimed to improve digital literacy skills through web-centric course learning for preservice teachers of Sultan Ageng Tirtayasa University. The method used in this study was a quasi-experiment through a pre-test and a post-test group design. The instrument used in this study was a digital literacy skills test in the Mathematical Profession English course, which was in the form of 20 essay tests. The material tested was the result of learning basic mathematics. In the process of learning mathematics, the preservice mathematics teacher had been introduced to number operations using English as opposed to the usual medium process of learning mathematics in the Indonesian Language. Furthermore, the instrument was tested on 20 preservice mathematics teachers of the said university. The average N-gain result for preservice mathematics teachers was 0.56 which belonged to the moderate category. So, it can be concluded that digital literacy skills in learning mathematics can be improved through the course.
Research on Face Detection Based on FeatherNet

research-article Research on Face Detection Based on FeatherNet Share on Authors: Zhiyong Liu Qilu University of Technology(Shandong Academy of Sciences), China Qilu University of Technology(Shandong Academy of Sciences), ChinaView Profile , FuQiang Wang Qilu University of Technology(Shandong Academy of Sciences), China Qilu University of Technology(Shandong Academy of Sciences), ChinaView Profile , Xiaoming Wu Qilu University of Technology(Shandong Academy of Sciences), China Qilu University of Technology(Shandong Academy of Sciences), ChinaView Profile Authors Info & Affiliations DMIP '20: 2020 3rd International Conference on Digital Medicine and Image ProcessingNovember 2020 Pages 51–55https://doi.org/10.1145/3441369.3441378Published:06 November 2020 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks1 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
Image super-resolution using multi-dictionary sparse representation

Image super-resolution (SR) is of great significance in image processing field by providing an alternative approach to overcome the resolution limitations of devices. Many sparse representation based methods use a single pair of dictionaries for image SR tasks, which are not suitable for reconstructing image patches with different structures. To overcome this issue, we propose a multi-dictionary sparse representation algorithm for SR image reconstruction in this study. Specifically, the stationary wavelet transform (SWT) is utilized to extract image features firstly. Then the Gaussian mixture model (GMM) is created to group image patches into clusters with each corresponding to a different dictionary pair. The high resolution image is reconstructed with multiple types of dictionary pairs together. Finally, the iterative back projection (IBP) method is applied to refine SR image. Experiment shows that the proposed algorithm obtains more reasonable results of SR image reconstruction and more promising performance than some state-of-the-art SR methods through quantitative and qualitative analysis.
Large-Sample Properties of Jackknife Estimators of the Variance of a Sample Quantile

We study for a finite d (≥ 1), the limit properties of the family of delete-d jackknife estimators of the variance of a sample quantile from a random sample of size n as n →∞. We consider central and intermediate sample quantiles and for the central case, we provide asymptotically unbiased delete-d jackknife estimators of its large-sample variance. In the intermediate case, the limit distribution of the delete-d jackknife estimator is free of d. For the sample median, the limit distributions of the delete-d jackknife estimators of its variance differ for sequences of odd and even values of n − d.
The Data, the Community and a Data-Led Identification of MWEs

Chapter 3 presents a new procedure for extracting formulaic language from corpus materials and further methodological aspects. In the first section, the speech community whose language is investigated is introduced and it is shown that the community of users of the Swiss variety of standard written German is a speech community sharing not only this common language variety but sharing in a culture that, at one level, is distinguished both from other linguistic communities within the country and from German-speaking communities elsewhere. The specifics of the linguistic data that form the basis of subsequent investigations are also presented. The second section concerns the identification of instances of formulaic language in corpus data. A robust operationalisation of the MWE-concept is presented, and the final section of the chapter documents a new procedure for extracting operationalisation-compliant sequences from corpus materials. It is shown that this procedure delivers accurate as well as comprehensive results and is therefore well suited for the purposes of the study.
Construction of GAN Dataset to Generate Images Similar to Copyrighted Images

In this paper, we propose a system for improving the convenience of using images on the Internet and SNS, and for preventing copyright infringement that people take without notice. In this system, many similar images are collected from one image on the Internet, and an image is generated from the collected images using deep learning. As a study of image collection means, we will conduct an experiment using Content-Based Image Retrieval (CBIR), which performs image retrieval using image features. In the experiment, images are searched using CBIR and similar images are collected to construct a dataset.
Hate Speech and Authentic Personhood: Unveiling the Truth

We often think that individuals should live their lives in ways they are convinced are most appropriate to their self-understanding and beliefs. However, we equally think that this idea of living according to one’s convictions and self-understanding does not mean endorsement of forms of speech that injure the person and dignity of others. So far, the debate on hate speech has revolved around the justification of its protection and prohibition. In this article, we examine the idea of hate speech through the lens of authentic personhood, the foundation upon which most arguments for and against speech restriction are based. The aim is to demonstrate that the state restricting speech may be as justified as the one protecting it, seeing that in reality, hate speech both violates and vindicates authentic personhood. Given the nature and structure of “autonomy” and “speech,” a balancing approach to free expression and equality is preferable to outright protection or prohibition of hate speech.
A Combined Wavelet and Variational Mode Decomposition Approach for Denoising Texture Images

Edges and textures are important features in texture analysis that helps to characterize an image. Thus, the edges and textures must be retained during the process of denoising. In this paper, we present a combined wavelet decomposition and Variational Mode Decomposition (VMD) approach to effectively denoise texture images while preserving the edges and fine-scale textures. The performance of the proposed method is compared with that of wavelet decomposition, VMD and a combined VMD-WT technique. Although VMD-WT outperforms VMD and wavelet decomposition, it is highly dependent on the choice of parameters. The proposed method overcomes the above limitation and also performs better than wavelet decomposition and VMD.
Sentiment Analysis and Review Classification using Deep Learning

Sentiment analysis or opinion mining is the method of deciding the emotions, opinions behind series of words, its accustomed gain an understanding of the attitudes, opinions and emotions expressed by the people. In recent years, research work is being performed in these fields by applying numerous methodologies. Sentiment analysis of social media content has become one of the most sought area among researchers because the number of product review sites, social networking sites, blogs, and forums are developing extensively. This field mainly utilizes supervised, unsupervised and semi-supervised technique for this sentiment prediction and classification task. This project studies the inability of widely used feature selection method like Information gain (IG) on machine learning and deep learning approaches. Initially, feature selection method called information gain has been used to select the feature subsets. In addition Naïve Bayes classification is done to find the probability of features found in all sub categories of reviews. Deep learning approach has been used in exact classification of new review from the given dataset and R language is used to develop the application.