2,256 research outputs found
Weighted-mean scheme for solving incompressible viscous flow
The problem of how a boundary layer responds to the motion of a convexed vortex on a porous wall was investigated. The wall velocity is approximately given by Darcy's law. The vorticity-stream function approach was adopted for solving Navier-Stokes equations of two dimensional incompressible viscous flows. The weighted-mean scheme was used for constructing finite difference approximations of spatial derivatives. Several test problems were solved and numerical results demonstrate clearly the accuracy, stability, and efficiency of the scheme. The weighted mean scheme then can be applied to the vortical flow problem
ADMINISTERING ONLINE EXAMS FROM HESITANTLY TO INNOVATIVELY - A PERSONAL PERSPECTIVE THROUGH AN ACTION RESEARCH
In the midst of COVID-19, university campuses were shut down and faculty had to move their classes online. The abrupt change opened up many challenges. One of them was how to handle online exams. This paper recounted how one instructor had managed the exams online. The paper presents this experience from an action research perspective. At the core is the narrative that captures the exam design process, the tools used, and the proctoring setup. The results are based on the interpretation of the phenomenon through a lens of the unified theory of acceptance and use of technology (UTAUT). The insights reveal some qualitative evidence to support relevant factors in UTAUT
Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen
Armed conflict has led to an unprecedented number of internally displaced
persons (IDPs) - individuals who are forced out of their homes but remain
within their country. IDPs often urgently require shelter, food, and
healthcare, yet prediction of when large fluxes of IDPs will cross into an area
remains a major challenge for aid delivery organizations. Accurate forecasting
of IDP migration would empower humanitarian aid groups to more effectively
allocate resources during conflicts. We show that monthly flow of IDPs from
province to province in both Syria and Yemen can be accurately forecasted one
month in advance, using publicly available data. We model monthly IDP flow
using data on food price, fuel price, wage, geospatial, and news data. We find
that machine learning approaches can more accurately forecast migration trends
than baseline persistence models. Our findings thus potentially enable
proactive aid allocation for IDPs in anticipation of forecasted arrivals
#REVAL: a semantic evaluation framework for hashtag recommendation
Automatic evaluation of hashtag recommendation models is a fundamental task
in many online social network systems. In the traditional evaluation method,
the recommended hashtags from an algorithm are firstly compared with the ground
truth hashtags for exact correspondences. The number of exact matches is then
used to calculate the hit rate, hit ratio, precision, recall, or F1-score. This
way of evaluating hashtag similarities is inadequate as it ignores the semantic
correlation between the recommended and ground truth hashtags. To tackle this
problem, we propose a novel semantic evaluation framework for hashtag
recommendation, called #REval. This framework includes an internal module
referred to as BERTag, which automatically learns the hashtag embeddings. We
investigate on how the #REval framework performs under different word embedding
methods and different numbers of synonyms and hashtags in the recommendation
using our proposed #REval-hit-ratio measure. Our experiments of the proposed
framework on three large datasets show that #REval gave more meaningful hashtag
synonyms for hashtag recommendation evaluation. Our analysis also highlights
the sensitivity of the framework to the word embedding technique, with #REval
based on BERTag more superior over #REval based on FastText and Word2Vec.Comment: 18 pages, 4 figure
Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs
In this paper, we revive the use of old-fashioned handcrafted video
representations for action recognition and put new life into these techniques
via a CNN-based hallucination step. Despite of the use of RGB and optical flow
frames, the I3D model (amongst others) thrives on combining its output with the
Improved Dense Trajectory (IDT) and extracted with its low-level video
descriptors encoded via Bag-of-Words (BoW) and Fisher Vectors (FV). Such a
fusion of CNNs and handcrafted representations is time-consuming due to
pre-processing, descriptor extraction, encoding and tuning parameters. Thus, we
propose an end-to-end trainable network with streams which learn the IDT-based
BoW/FV representations at the training stage and are simple to integrate with
the I3D model. Specifically, each stream takes I3D feature maps ahead of the
last 1D conv. layer and learns to `translate' these maps to BoW/FV
representations. Thus, our model can hallucinate and use such synthesized
BoW/FV representations at the testing stage. We show that even features of the
entire I3D optical flow stream can be hallucinated thus simplifying the
pipeline. Our model saves 20-55h of computations and yields state-of-the-art
results on four publicly available datasets.Comment: First two authors contributed equally. This paper is accepted by
ICCV'1
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