729 research outputs found
Using Social Media Tools for Collaborative Learning: A Mixed-Method Investigation on Academic Group Work by iSchool Students around the World
Collaborative learning helps university students improve their academic achievement,
learning persistence and attitudes (Springer et al., 1999). Social media were found to have
positive effects on collaborative learning by encouraging positive interactions online (Al-Rahmi
et al., 2014; Thalluri & Penman, 2015). This mixed-method dissertation research
investigates how social media tools help to facilitate collaborative learning activities of iSchools
students around the world. It included an online survey (Phase I) with over 300 iSchool students
from 26 iSchools in 9 countries/regions, followed by 31 in-depth interviews (Phase II). The focal
areas of the investigation are: 1) the factors influencing iSchools students’ selection of social
media tools; 2) the needed features and functions of social media for collaborative learning
activities; 3) collaboration and communication strategies of iSchools students; and 4) the impacts
of design characteristics, usability, and UX aspects of the social media tools on iSchools
students’ collaborative learning. The preliminary analysis results revealed that both effective
social media functions and students’ high proficiency of using social media tools were vital for a
successful collaboration, however it was unlikely that both were present to achieve successful
collaborative learning.
This dissertation research fills the gap of the research studies on collaborative learning
using social media tools and usability requirements associated with using social media for
learning purposes. In the long run, the study results provide evidence for improving the design of
group assignments and team-based projects for collaborative learning in iSchools and beyond
Homologous recombination and directed differentiation in medaka ES cells: Development of vector systems
Master'sMASTER OF SCIENC
Framework of mobile-based learning (M-Learning): An exploratory study on the use of mobile devices for university students’ academic learning
This paper reports the results of 15 in-depth interviews with university students in the Greater Boston area regarding their mobile learning experiences, including the kinds of learning activities performed, and the advantages and challenges of m-learning. Mobile devices were used mainly for initial exploratory learning or a way for quick access and interacting with classmates. Participants avoided using mobile devices for complicated tasks or deep learning. The limited usability of mobile devices in supporting advanced learning is alarming. A conceptual framework of m-learning containing dimensions of mobility and ubiquity, convenience, interaction and collaboration, and usability was presented
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)
For efficient query processing, DBMS query optimizers have for decades relied
on delicate cardinality estimation methods. In this work, we propose an
Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ
queries. The core idea is to discover the implicit relationships between
queries and underlying dynamic data using attention mechanisms in ALECE's two
modules that are built on top of carefully designed featurizations for data and
queries. In particular, from all attributes in the database, the data-encoder
module obtains organic and learnable aggregations which implicitly represent
correlations among the attributes, whereas the query-analyzer module builds a
bridge between the query featurizations and the data aggregations to predict
the query's cardinality. We experimentally evaluate ALECE on multiple dynamic
workloads. The results show that ALECE enables PostgreSQL's optimizer to
achieve nearly optimal performance, clearly outperforming its built-in
cardinality estimator and other alternatives.Comment: VLDB 202
DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. However, despite the significant progress that has been made in generic image synthesis using diffusion models, producing garment images with garment part level semantics that are well aligned with input text prompts and then flexibly manipulating the generated results still remains a problem. Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain. In this work, we instead introduce DiffCloth, a diffusion-based pipeline for cross-modal garment synthesis and manipulation, which empowers diffusion models with flexible compositionality in the fashion domain by structurally aligning the cross-modal semantics. Specifically, we formulate the part-level cross-modal alignment as a bipartite matching problem between the linguistic Attribute-Phrases (AP) and the visual garment parts which are obtained via constituency parsing and semantic segmentation, respectively. To mitigate the issue of attribute confusion, we further propose a semantic-bundled cross-attention to preserve the spatial structure similarities between the attention maps of attribute adjectives and part nouns in each AP. Moreover, DiffCloth allows for manipulation of the generated results by simply replacing APs in the text prompts. The manipulation-irrelevant regions are recognized by blended masks obtained from the bundled attention maps of the APs and kept unchanged. Extensive experiments on the CM-Fashion benchmark demonstrate that DiffCloth both yields state-of-the-art garment synthesis results by leveraging the inherent structural information and supports flexible manipulation with region consistency
DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
Cross-modal garment synthesis and manipulation will significantly benefit the
way fashion designers generate garments and modify their designs via flexible
linguistic interfaces.Current approaches follow the general text-to-image
paradigm and mine cross-modal relations via simple cross-attention modules,
neglecting the structural correspondence between visual and textual
representations in the fashion design domain. In this work, we instead
introduce DiffCloth, a diffusion-based pipeline for cross-modal garment
synthesis and manipulation, which empowers diffusion models with flexible
compositionality in the fashion domain by structurally aligning the cross-modal
semantics. Specifically, we formulate the part-level cross-modal alignment as a
bipartite matching problem between the linguistic Attribute-Phrases (AP) and
the visual garment parts which are obtained via constituency parsing and
semantic segmentation, respectively. To mitigate the issue of attribute
confusion, we further propose a semantic-bundled cross-attention to preserve
the spatial structure similarities between the attention maps of attribute
adjectives and part nouns in each AP. Moreover, DiffCloth allows for
manipulation of the generated results by simply replacing APs in the text
prompts. The manipulation-irrelevant regions are recognized by blended masks
obtained from the bundled attention maps of the APs and kept unchanged.
Extensive experiments on the CM-Fashion benchmark demonstrate that DiffCloth
both yields state-of-the-art garment synthesis results by leveraging the
inherent structural information and supports flexible manipulation with region
consistency.Comment: accepted by ICCV202
Enhancing the Spin–Orbit Coupling in Fe3O4 Epitaxial Thin Films by Interface Engineering
10.1021/acsami.6b0947884027353-2735
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