13,330 research outputs found
Аналіз відмінностей тем, якості письма та стилістичного контексту в есеях студентів коледжу на основі комп’ютерної програми Linguistic Inquiry and Word Count (LIWC).
Machine methods for automatically analyzing text have been investigated for
decades. Yet the availability and usability of these methods for classifying and scoring specialized
essays in small samples–as is typical for ordinary coursework–remains unclear. In this paper we
analyzed 156 essays submitted by students in a first-year college rhetoric course. Using cognitive
and affective measures within Linguistic Inquiry and Word Count (LIWC), we tested whether
machine analyses could i) distinguish among essay topics, ii) distinguish between high and low
writing quality, and iii) identify differences due to changes in rhetorical context across writing
assignments. The results showed positive results for all three tests. We consider ways that LIWC
may benefit college instructors in assessing student compositions and in monitoring the
effectiveness of the course curriculum. We also consider extensions of machine assessments for
instructional applications.Машинні методи автоматичного аналізу тексту та їхні можливості
вивчалися впродовж десятиліть. Однак питання доступності та зручності використання цих
методів для класифікації та оцінки спеціалізованих есеїв у невеликих зразках, як,
наприклад, курсових роботах, залишається досі малодослідженим питанням. У статті
проаналізовано 139 есеїв із курсу стилістики, написаних студентами першого курсу. На
основі використання когнітивних та афективних категорій програми Linguistic Inquiry and
Word Count (LIWC) було перевірено здатність машинного аналізу: а) розмежовувати теми
есеїв, б) розрізняти високу та низьку якість письма та в) виявляти відмінності через зміни
стилістичного контексту написаних завдань. Дослідження засвідчило позитивні результати
для всіх трьох тестових перевірок. Увагу авторів зосереджено на тому, як LIWC може
полегшити роботу університетських викладачів під час оцінки ними студентських творів та
моніторингу ефективності навчальної програми курсу. Крім того, у статті розглянуто
питання перспектив машинного оцінювання викладацьких застосунків
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
Multimodal Prediction based on Graph Representations
This paper proposes a learning model, based on rank-fusion graphs, for
general applicability in multimodal prediction tasks, such as multimodal
regression and image classification. Rank-fusion graphs encode information from
multiple descriptors and retrieval models, thus being able to capture
underlying relationships between modalities, samples, and the collection
itself. The solution is based on the encoding of multiple ranks for a query (or
test sample), defined according to different criteria, into a graph. Later, we
project the generated graph into an induced vector space, creating fusion
vectors, targeting broader generality and efficiency. A fusion vector estimator
is then built to infer whether a multimodal input object refers to a class or
not. Our method is capable of promoting a fusion model better than early-fusion
and late-fusion alternatives. Performed experiments in the context of multiple
multimodal and visual datasets, as well as several descriptors and retrieval
models, demonstrate that our learning model is highly effective for different
prediction scenarios involving visual, textual, and multimodal features,
yielding better effectiveness than state-of-the-art methods
Physics based supervised and unsupervised learning of graph structure
Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss\u27s law, (3) feedback loops, and (3) neural synapses, to discover patterns in graphs
Deep convolutional embedding for digitized painting clustering
Clustering artworks is difficult because of several reasons. On one hand,
recognizing meaningful patterns in accordance with domain knowledge and visual
perception is extremely hard. On the other hand, the application of traditional
clustering and feature reduction techniques to the highly dimensional pixel
space can be ineffective. To address these issues, we propose a deep
convolutional embedding model for clustering digital paintings, in which the
task of mapping the input raw data to an abstract, latent space is optimized
jointly with the task of finding a set of cluster centroids in this latent
feature space. Quantitative and qualitative experimental results show the
effectiveness of the proposed method. The model is also able to outperform
other state-of-the-art deep clustering approaches to the same problem. The
proposed method may be beneficial to several art-related tasks, particularly
visual link retrieval and historical knowledge discovery in painting datasets
MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds
Global wind observations are fundamental for studying weather and climate dynamics and for operational forecasting. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques for their height assignments, which are subject to large uncertainties in the presence of weak or reversed vertical temperature gradients near the planetary boundary layer (PBL)and tropopause folds. Stereo imaging can overcome the height assignment problem using geometric parallax for feature height determination. In this study we develop a stereo 3D-Wind algorithm to simultaneously retrieve AMV and height from geostationary (GEO) and low Earth orbit (LEO) satellite imagery and apply it to collocated Geostationary Operational Environmental Satellite (GOES)and Multi-angle Imaging SpectroRadiometer (MISR) imagery. The new algorithm improves AMV and height relative to products from GOES or MISR alone, with an estimated accuracy of <0.5 m/s in AMV and <200 m in height with 2.2 km sampling. The algorithm can be generalized to other LEO-GEO or LEO-LEO combinations for greater spatiotemporal coverage. The technique demonstrated with MISR and GOES has important implications for future high-quality AMV observations, for which a low-cost constellation of CubeSats can play a vital role
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