97 research outputs found
Control and Evaluation of Slow-Active Suspensions with Preview for a Full Car
An optimal control design method based on the use of the correlation between the front and rear wheel inputs (wheelbase preview) is introduced and then applied to the optimum design of a slow-active suspension system. The suspension consists of a limited bandwidth actuator in series with a passive spring, the combination being in parallel with a passive damper. A three-dimensional seven degrees of freedom car riding model subjected to four correlated random road inputs is considered. The performance potential of the limited bandwidth system with wheelbase preview in comparison with the nonpreview (uncorrelated inputs) case is investigated
Towards Understanding Egyptian Arabic Dialogues
Labelling of user's utterances to understanding his attends which called
Dialogue Act (DA) classification, it is considered the key player for dialogue
language understanding layer in automatic dialogue systems. In this paper, we
proposed a novel approach to user's utterances labeling for Egyptian
spontaneous dialogues and Instant Messages using Machine Learning (ML) approach
without relying on any special lexicons, cues, or rules. Due to the lack of
Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus
includes 4725 utterances for three domains, which are collected and annotated
manually from Egyptian call-centers. The system achieves F1 scores of 70. 36%
overall domains.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0308
Holy Tweets: Exploring the Sharing of the Quran on Twitter
While social media offer users a platform for self-expression, identity
exploration, and community management, among other functions, they also offer
space for religious practice and expression. In this paper, we explore social
media spaces as they subtend new forms of religious experiences and rituals. We
present a mixed-method study to understand the practice of sharing Quran verses
on Arabic Twitter in their cultural context by combining a quantitative
analysis of the most shared Quran verses, the topics covered by these verses,
and the modalities of sharing, with a qualitative study of users' goals. This
analysis of a set of 2.6 million tweets containing Quran verses demonstrates
that online religious expression in the form of sharing Quran verses both
extends offline religious life and supports new forms of religious expression
including goals such as doing good deeds, giving charity, holding memorials,
and showing solidarity. By analysing the responses on a survey, we found that
our Arab Muslim respondents conceptualize social media platforms as
everlasting, at least beyond their lifetimes, where they consider them to be
effective for certain religious practices, such as reciting Quran, supplication
(dua), and ceaseless charity. Our quantitative analysis of the most shared
verses of the Quran underlines this commitment to religious expression as an
act of worship, highlighting topics such as the hereafter, God's mercy, and
sharia law. We note that verses on topics such as jihad are shared much less
often, contradicting some media representation of Muslim social media use and
practice.Comment: Paper accepted to The 23rd ACM Conference on Computer-Supported
Cooperative Work and Social Computing (CSCW) 202
Role of Rapid Antigen Test for Covid 19 in Family Medicine Outpatient Clinic
COVID-19 is a worldwide medical problem affecting majority of people with different age groups, where family medicine outpatient clinics is the first line in detecting and managing this medical issue, we tried to put our hands to explore the role of rapid antigent test for covid 19 in family medicine outpatent clinic and its effectiveness
On the Robustness of Arabic Speech Dialect Identification
Arabic dialect identification (ADI) tools are an important part of the
large-scale data collection pipelines necessary for training speech recognition
models. As these pipelines require application of ADI tools to potentially
out-of-domain data, we aim to investigate how vulnerable the tools may be to
this domain shift. With self-supervised learning (SSL) models as a starting
point, we evaluate transfer learning and direct classification from SSL
features. We undertake our evaluation under rich conditions, with a goal to
develop ADI systems from pretrained models and ultimately evaluate performance
on newly collected data. In order to understand what factors contribute to
model decisions, we carry out a careful human study of a subset of our data.
Our analysis confirms that domain shift is a major challenge for ADI models. We
also find that while self-training does alleviate this challenges, it may be
insufficient for realistic conditions
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