Reviewerly/exHarmony huggingface dataset:
The Menagerie after Vahni Capildeo

Research Article| November 01 2019 The Menagerie after Vahni Capildeo Jasmine Throckmorton Jasmine Throckmorton Jasmine Throckmorton is a writer and equestrian. She now manages the family stables in Colorado, after pursuing an MA in creative writing at University College Cork in Ireland with the support of a Fulbright fellowship. Her work has appeared in Quarryman, Soliloquies Anthology, America, the Portland Review, and elsewhere. Search for other works by this author on: This Site Google the minnesota review (2019) 2019 (93): 17. https://doi.org/10.1215/00265667-7737031 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Jasmine Throckmorton; The Menagerie after Vahni Capildeo. the minnesota review 1 November 2019; 2019 (93): 17. doi: https://doi.org/10.1215/00265667-7737031 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. Copyright © 2019 Jasmine Throckmorton2019 Article PDF first page preview Close Modal You do not currently have access to this content.
3D shape measurement of convex aspheric surface using reverse Hartmann test

Based on the reflection of sinusoidal fringes and their distortions due to surface deviations from the ideal geometry, a method, called the reverse Hartmann test method, for the three-dimensional shape measurement of convex aspheric surfaces was developed. This method has an accuracy comparable to that of interferometry, in addition to its simple structure, low cost, and robustness against environmental turbulence and vibration. The developed method, which is based on the relative geometric position, is free from high-precision physical benchmarks. Furthermore, accurate surface data can be obtained through iterative fitting of Zernike slope polynomials without the need of complex calibration. Implementation of this measurement method will enable solving the problems associated with the measurement of convex aspheric surfaces with high dynamic range, high resolution, and high precision.
Dense and Partial Correspondence in Non-parametric Scene Parsing

Scene correspondence, in general, refers to the problem of finding a set of points on one image which corresponds to a set of points in another image. Given the corresponding points on a pair of images, the task of scene correspondence is to align these set of points for categorically labeling the pixels in the image. There are broadly two approaches of finding correspondence between scenes or images: (i) dense correspondence and (ii) partial correspondence. Dense correspondence is a pixel-by-pixel alignment of the scenes. Partial correspondence is a superpixel, patch, or region-based alignment of the scenes. Partial correspondence has the ability to align a large, cohesive group of pixels simultaneously. This paper provides a compact review of the state-of-the-art methods in the past few years for scene correspondence in the non-parametric scene parsing.
Effects of Data Reduction Methods and Rates on Classifiers

This paper addresses the effect of data reduction on speeding up training while keeping or improving the accuracy performance of classification. Since many studies have focused on feature selection, but did not adequately consider instance selection, our work focuses on both instance reduction and feature reduction, integrated into a whole reduction method. We examined in prior work Simple Random Sample Selection without Replacement, integrated with the Information Gain-based Feature Selection method, and compared its performance with the unintegrated instance selection and feature selection alone, using a single reduction rate. Our results proved that the integration of instance and feature selection performed much better than instance or feature selection alone. In this paper, we examine our approach in more depth, trying different reduction rates and different distributions of reduction rates between instance reduction and feature reduction. Our results show that for nearly all common classifiers, our integrated data reduction speeds up training significantly while keeping the accuracy unchanged (and sometimes even improved) at even high reduction rates. We also present the optimal feature-instance reduction-rates tradeoff.
Analysing and Applying Captured Object with Machine Learning Techniques

Detecting an object either form a static or dynamic image is considered to be a fundamental objective in the area of a computer vision. Object detection techniques can be applied both to static images as well as for dynamic images. Presently there subsist implementations of applications that uses object detection; this work orients itself on object detection using artificial intelligence and machine learning techniques implementing varied computer vision characteristics. The aim is to integrate state-of-the-art practice for object recognition with the intension of attaining extraordinary accurateness with an instantaneous enactment. The basic working is to identify patterns while capturing objects using camera to identify different daily tasks-and implementing them using gesturing. The final result exhibits real-time performance and satisfactory toning outcomes.
Outsourced Data Integrity Auditing for Efficient Batch Dynamic Updates

Cloud storage is becoming more and more popular as it provides a good solution for people with insufficient storage space. Provable Data Possession (PDP) is a model that allows to verify the outsourced data’s integrity without downloading it. However, existing dynamic data possession verification schemes not only suffer from low efficiency of batch auditing for multi-block Data, but also lack effective mechanism to update multiple blocks at the same time. In this paper, we propose a new dynamic provable data possession scheme for secure cloud data auditing. The scheme leverages BLS signatures and RMHT to support batch auditing and then optimizes batch auditing scenarios with four algorithms to support efficient batch updates. The theoretical analysis show the security of our scheme, and the experimental results show that the scheme has advantages over the existing dynamic integrity audit scheme in terms of computing time and communication cost.
A dataset for evaluating one-shot categorization of novel object classes

With the advent of deep convolutional neural networks, machines now rival humans in terms of object categorization. The neural networks solve categorization with a hierarchical organization that shares a striking resemblance to their biological counterpart, leading to their status as a standard model of object recognition in biological vision. Despite training on thousands of images of object categories, however, machine-learning networks are poorer generalizers, often fooled by adversarial images with very simple image manipulations that humans easily distinguish as a false image. Humans, on the other hand, can generalize object classes from very few samples. Here we provide a dataset of novel object classifications in humans. We gathered thousands of crowd-sourced human responses to novel objects embedded either with 1 or 16 context sample(s). Human decisions and stimuli together have the potential to be re-used (1) as a tool to better understand the nature of the gap in category learning from few samples between human and machine, and (2) as a benchmark of generalization across machine learning networks.
Adaptively Secure Puncturable Pseudorandom Functions via Puncturable Identity-Based KEMs

In this paper, we are interested in constructing Puncturable Pseudorandom Functions (PPRFs), a special class of constrained PRFs. While selectively secure PPRFs can be constructed from GGM tree-based PRFs, the adaptive counterpart is tricky to deal with. Inspired by previous works, we investigate on the possibility of directly obtaining adaptively-secure PPRF from Puncturable Identity-based Key Encapsulation Mechanism (PIB-KEM). Our contributions can be summarized as follows: (i) we show that one could derive adaptively-secure PPRFs very naturally originating from PIB-KEM satisfying two necessary conditions. (ii) we define t-puncturable IB-KEM (t-PIBKEM) and show its existence by an efficient conversion basing on Hierarchical IB-KEM (HIB-KEM). Furthermore, we demonstrate its application to constructing t-puncturable PRFs, a generalized notion of PPRFs.
Unsupervised Answer Retrieval with Data Fusion for Community Question Answering

Community question answering (cQA) systems have enjoyed the benefits of advances in neural information retrieval, some models of which need annotated documents as supervised data. However, in contrast with the amount of supervised data for cQA systems, user-generated data in cQA sites have been increasing greatly with time. Thus, focusing on unsupervised models, we tackle a task of retrieving relevant answers for new questions from existing cQA data and propose two frameworks to exploit a Question Retrieval (QR) model for Answer Retrieval (AR). The first framework ranks answers according to the combined scores of QR and AR models and the second framework ranks answers using the scores of a QR model and best answer flags. In our experiments, we applied the combination of our proposed frameworks and a classical fusion technique to AR models with a Japanese cQA data set containing approximately 9.4M question-answer pairs. When best answer flags in the cQA data cannot be utilized, our combination of AR and QR scores with data fusion outperforms a base AR model on average. When best answer flags can be utilized, the retrieval performance can be improved further. While our results lack statistical significance, we discuss effect sizes as well as future sample sizes to attain sufficient statistical power.
Label propagation algorithm based on Roll-back detection and credibility assessment

The traditional label propagation algorithm (LPA) iteratively propagates labels from a small number of labeled samples to many unlabeled ones based on the sample similarities. However, due to the randomness of label propagations, and LPA's weak ability to deal with uncertain points, the label error may be continuously expanded during the propagation process. In this paper, the algorithm label propagation based on roll-back detection and credibility assessment (LPRC) is proposed. A credit evaluation of the unlabeled samples is carried out before the selection of samples in each round of label propagation, which makes sure that the samples with more certainty can be labeled first. Furthermore, a roll-back detection mechanism is introduced in the iterative process to improve the label propagation accuracy. At last, our method is compared with 9 algorithms based on UCI datasets, and the results demonstrated that our method can achieve better classification performance, especially when the number of labeled samples is small. When the labeled samples only account for 1% of the total sample number of each synthetic dataset, the classification accuracy of LPRC improved by at least 26.31% in dataset circles, and more than 13.99%, 15.22% than most of the algorithms compared in dataset moons and varied, respectively. When the labeled samples account for 2% of the total sample number of each dataset in UCI datasets, the accuracy (take the average value of 50 experiments) of LPRC improved in an average value of 23.20% in dataset wine, 20.82% in dataset iris, 4.25% in dataset australian, and 6.75% in dataset breast. And the accuracy increases with the number of labeled samples.
Analyzing Relationship between the Atmospheric State Change and Lightning Activity in Autumn Season

An analysis between the atmospheric state change and lightning discharges in the autumn (from Sep to Nov. 2011 and 2016) seasons in Japan is presented in this paper. In evaluating the number of lightning activities, we calculated SSI which is used to predict thunderstorms. We confirmed that the modified SSI for the winter season can be effective for evaluating the lighting activities in the autumn season, meaning that the lighting discharges in this season tend to be affected by the low-level atmospheric state condition.
Big Data Dependability Opportunities &amp; Challenges

Big Data is a very large data set, its analysis exceeds the capabilities of traditional database management systems. Big Data is linked to the need for large computing and storage capacity.Big Data dependability is one of the major concerns of organizations. It reflects the confidence that can be placed in these data. Nowadays, companies find a major interest in Big Data, but dependability challenge remains a major obstacle.In this article, we present different works that have addressed Big Data dependability aspects. This study highlights new opportunities in this field as well as different challenges.
Concave Point Extraction: A Novel Method for WBC Segmentation in ALL Images

Analysis of hematological disorder of leukocyte (WBC) is common in medical diagnoses. The counting and classification of blood cells enables the estimation and identification of large number of diseases. Morphological analysis of blood cells requires skill operators, is time-consuming, and less accurate. This paper discusses the Computer-Aided Acute Lymphoblastic Leukemia (ALL) diagnosis technique based on image examination. Here, methods for the segmentation and categorization of white blood cell (WBC) in the interior of acute lymphoblastic leukemia (ALL) smear images are presented. The segmentation method consists of contour evidence extrication to detect the apparent part of each entity and contour estimation to evaluate the final object contours. Then, some attributes were extracted from each segmented cell. In this paper, detection of hematological disorders like leukemia (blood cancer) is carried out which is based on geometric, texture, and mixed features. Classification is done to categories each segmented cell to be normal (benign) or abnormal (malignant).
Particulate Matter Estimation Based on Haze Degree Factor

In recent years, study of airborne particles in atmosphere develops interest for researcher to contribute in this field. Exposure of these fine particles can cause various human diseases. Estimation of particulate matter characterization form digital images is a tedious task. There are numerous traditional methods to predict the characterization of these fine particles. Image analysis plays a key role to estimate the particulate matter. Here in this paper, we develop a method to estimate particulate matter based upon haziness present in digital images. Digital images are taken from Achieve of Many Outdoor Scene (AMOS) dataset. Image features were extracted from these images in contrast with some relevant data such as weather conditions, geographical locations, date, time, and position of the sun rays to predict the particulate matter index. Haziness value is being calculated and compared after this image analysis process to predict the particulate matter. Experimental results yield to predict the accurate particulate matter present in AMOS dataset images.
Swarm Optimization techniques using Attribute assortment and Clustering for Machine Learning Classifiers

Software defined Networking addresses the growth of traffic with static architectures. SDN is a standard which separates the network structures. Quality of Service is used with network traffic to transfer high bandwidth and multimedia information. Fractional Order Darwinian optimization (FODPSO) is used with Particle Swarm Optimization algorithm to enhance the detection accuracy. In this research paper, comparison of different Classification algorithms are used to achieve better performance.
Detection of zones based on ore and gangue using fractal and multivariate analysis in Chah Gaz iron ore deposit, Central Iran

Detection of mineralized zones based on ores and gangues is important for mine planning and excavation operation. Major goal of this research is determination of zones based on ores and gangues by combination of fractal and factor analysis in Chah Gaz iron ore (Central Iran). Concentration-Volume (C-V) fractal method was carried out for Fe and S which indicates the main mineralized zones consist of Fe, S and P values ≥57%, ≤0.4% and ≤0.3%, respectively. Factor analysis categorized variables in two groups including factor 1 (F1) and factor 2 (F2) for ore and gangue, respectively. The C-V fractal modeling on the derived factors shows four zones for the F1 and F2. Based on the correlation among results of fractal modeling on the elements and factors, first and second zones of F1 are proper for exploitation. Furthermore, the last and first zones of F1 and F2 can be assumed as main waste for mining excavation.
Motion History and Shape Orientation Based Human Action Analysis

In recent decades, many research works focused on the considerations of falling and post-falling event analysis in the aspect of image processing technology to meet the consumer perspective of users. In this paper, the more informative considerations of human action analysis are developed for the prior state of fall or normal actions. In doing so, the effective background subtraction namely the Mixture of Gaussian with Low-Rank Matrix Factorization model is used to obtain robust and adaptive foreground from the practical video sequences. Then, the spatial-temporal bilateral grid for video sequences is constructed by using a standard graph cut theory to improve the cleaned foreground in order to detect the moving/motionless object region. Then, human actions are analyzed by employing motion history and shape orientation using the approximated ellipse method. The experiments are conducted on publicly available video sequences and our simulated video sequences.
Native Language Identification for Russian

The task of recognizing the author’s native language based on a text (Native Language Identification - NLI) is the task of automatically recognizing native language (L1) based on texts written in a language that is not native to the author. The NLI task was studied in detail for the English language, and two shared tasks were conducted in 2013 [1] and 2017 [2], where TOEFL English essays and essay samples were used as data. There is also a small number of works where the NLI problem was solved for other languages, among which Russian has not yet been studied. This paper discusses the use of well-established approaches in the NLI Shared Task 2013 and 2017 competitions to solve the problem of recognizing the author's native language, as well as to recognize the type of speaker — learners of Russian or Heritage Russian speakers. The classifier presented in this paper is based on the support vector machine (SVM) using the TF-IDF metric. This study is data-driven and is possible thanks to the Russian Learner Corpus developed by the HSE Learner Russian Research Group [3] on the basis of which experiments are being conducted.
Factors Influencing the Adoption of Agile Methodology within SMEs in Cape Town

There are many software development Small, Medium enterprises (SMEs) in Cape Town, that are continuing to make use of the legacy and rigid waterfall software development methodology. The study therefore, was to identify and understand the factors which influence the adoption of an agile methodology within SMEs in Cape Town, South Africa. The study used the technological-organizational-environmental framework to obtain those factors to understand which, if not all of them, influence the adoption of an agile methodology. The study followed a qualitative approach and six SMEs across various industries were interviewed. The study results showed few factors that influence the adoption of agile methodology within SMES in Cape Town. The study results showed that there is an interest among SMES to adopt and implement an agile methodology. The study contributes to the body of knowledge on the factors influencing the adoption of an agile methodology Cape Town SMEs.
Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects.Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g.purchase history, moviewatching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning.That is RecSys has considered as a sequence prediction problem.However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences.For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture.This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts.Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.
Adding context to Concept Trees

A Concept Tree is a structure for representing knowledge, where the trees are stored in a Concept Base. It sits between the highly distributed neural architectures and the distributed information systems, with the intention of bringing brain-like and computer systems closer together. Concept Trees can grow from unstructured or semi-structured sources when consistent sequences are presented. Each tree ideally represents a single cohesive concept and the trees can link with each other for navigation and semantic purposes. A consistent build process is managed through a 'counting rule' and mathematical basis that can normalise the database structure. This restricted structure can then be complimented and enriched by a more dynamic context. A number of tests have demonstrated the soundness of the architecture, which may be inherent in natural language. Two types of query language are also described that can answer knowledge-based questions and even enhance the query with new knowledge.
Exploring the Student’s Interaction with Augmented Reality and Their Relationship to Learning Achievement

Augmented Reality (AR) is one of the latest technology that has grown significantly due to its effectiveness in various fields includein education field. AR can be defined as a technology where virtual object can be overlaid into real environment in real time. AR can serve as an effective tool to ensure the learning process more attractive and engage the student.However, previous studies on AR have not provided an in-depth investigation of the learning process, especially the interaction involved in student while they use an AR. Therefore, this study aimed to determine the student interaction and their performance in subject content, and their relationship between them while using AR. A quantitative research design was employed with 34 grade 5 elementary school students as respondents. The results of the content analysis towards student interaction with the AR demonstrates that turning the AR markers, inspecting the AR elements and commenting on the AR elements are recorded as the highest number of interactions compared to others. Results from a Spearman correlation analysis show that there are six interactions that have significant correlations with achievement in science among the students with the correlation direction of each relationship is positive.