195 research outputs found
Content-Localization based Neural Machine Translation for Informal Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic
Resources in high-resource languages have not been efficiently exploited in
low-resource languages to solve language-dependent research problems. Spanish
and French are considered high resource languages in which an adequate level of
data resources for informal online social behavior modeling, is observed.
However, a machine translation system to access those data resources and
transfer their context and tone to a low-resource language like dialectal
Arabic, does not exist. In response, we propose a framework that localizes
contents of high-resource languages to a low-resource language/dialects by
utilizing AI power. To the best of our knowledge, we are the first work to
provide a parallel translation dataset from/to informal Spanish and French
to/from informal Arabic dialects. Using this, we aim to enrich the
under-resource-status dialectal Arabic and fast-track the research of diverse
online social behaviors within and across smart cities in different
geo-regions. The experimental results have illustrated the capability of our
proposed solution in exploiting the resources between high and low resource
languages and dialects. Not only this, but it has also been proven that
ignoring dialects within the same language could lead to misleading analysis of
online social behavior.Comment: arXiv admin note: text overlap with arXiv:2312.0372
Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf
Even though online social movements can quickly become viral on social media,
languages can be a barrier to timely monitoring and analyzing the underlying
online social behaviors (OSB). This is especially true for under-resourced
languages on social media like dialectal Arabic; the primary language used by
Arabs on social media. Therefore, it is crucial to provide solutions to
efficiently exploit resources from high-resourced languages to solve
language-dependent OSB analysis in under-resourced languages. This paper
proposes to localize content of resources in high-resourced languages into
under-resourced Arabic dialects. Content localization goes beyond content
translation that converts text from one language to another; content
localization adapts culture, language nuances and regional preferences from one
language to a specific language/dialect. Automating understanding of the
natural and familiar day-to-day expressions in different regions, is the key to
achieve a wider analysis of OSB especially for smart cities. In this paper, we
utilize content-localization based neural machine translation to develop
sentiment and hate classifiers for two low-resourced Arabic dialects: Levantine
and Gulf. Not only this but we also leverage unsupervised learning to
facilitate the analysis of sentiment and hate predictions by inferring hidden
topics from the corresponding data and providing coherent interpretations of
those topics in their native language/dialects. The experimental evaluations
and proof-of-concept COVID-19 case study on real data have validated the
effectiveness of our proposed system in precisely distinguishing sentiments and
accurately identifying hate content in both Levantine and Gulf Arabic dialects.
Our findings shed light on the importance of considering the unique nature of
dialects within the same language and ignoring the dialectal aspect would lead
to misleading analysis
Interacting with New York City Data by HoloLens through Remote Rendering
In the digital era, Extended Reality (XR) is considered the next frontier.
However, XR systems are computationally intensive, and they must be implemented
within strict latency constraints. Thus, XR devices with finite computing
resources are limited in terms of quality of experience (QoE) they can offer,
particularly in cases of big 3D data. This problem can be effectively addressed
by offloading the highly intensive rendering tasks to a remote server.
Therefore, we proposed a remote rendering enabled XR system that presents the
3D city model of New York City on the Microsoft HoloLens. Experimental results
indicate that remote rendering outperforms local rendering for the New York
City model with significant improvement in average QoE by at least 21%.
Additionally, we clarified the network traffic pattern in the proposed XR
system developed under the OpenXR standard
Learning to Estimate 3D Human Pose from Point Cloud
3D pose estimation is a challenging problem in computer vision. Most of the
existing neural-network-based approaches address color or depth images through
convolution networks (CNNs). In this paper, we study the task of 3D human pose
estimation from depth images. Different from the existing CNN-based human pose
estimation method, we propose a deep human pose network for 3D pose estimation
by taking the point cloud data as input data to model the surface of complex
human structures. We first cast the 3D human pose estimation from 2D depth
images to 3D point clouds and directly predict the 3D joint position. Our
experiments on two public datasets show that our approach achieves higher
accuracy than previous state-of-art methods. The reported results on both ITOP
and EVAL datasets demonstrate the effectiveness of our method on the targeted
tasks
Towards a QoE Model to Evaluate Holographic Augmented Reality Devices
Augmented reality (AR) technology is developing fast and provides users with
new ways to interact with the real-world surrounding environment. Although the
performance of holographic AR multimedia devices can be measured with
traditional quality-of-service parameters, a quality-of-experience (QoE) model
can better evaluate the device from the perspective of users. As there are
currently no well-recognized models for measuring the QoE of a holographic AR
multimedia device, we present a QoE framework and model it with a fuzzy
inference system to quantitatively evaluate the device
A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN
Accurate Defect detection is crucial for ensuring the trustworthiness of
intelligent railway systems. Current approaches rely on single deep-learning
models, like CNNs, which employ a large amount of data to capture underlying
patterns. Training a new defect classifier with limited samples often leads to
overfitting and poor performance on unseen images. To address this, researchers
have advocated transfer learning and fine-tuning the pre-trained models.
However, using a single backbone network in transfer learning still may cause
bottleneck issues and inconsistent performance if it is not suitable for a
specific problem domain. To overcome these challenges, we propose a reusable
AI-enabled defect detection approach. By combining ensemble learning with
transfer learning models (VGG-19, MobileNetV3, and ResNet-50), we improved the
classification accuracy and achieved consistent performance at a certain phase
of training. Our empirical analysis demonstrates better and more consistent
performance compared to other state-of-the-art approaches. The consistency
substantiates the reusability of the defect detection system for newly evolved
defected rail parts. Therefore we anticipate these findings to benefit further
research and development of reusable AI-enabled solutions for railway systems.Comment: 28 pages, 13 Figures, Applied Intelligence Journal, Springer Natur
Sitting Posture Recognition Using a Spiking Neural Network
To increase the quality of citizens' lives, we designed a personalized smart
chair system to recognize sitting behaviors. The system can receive surface
pressure data from the designed sensor and provide feedback for guiding the
user towards proper sitting postures. We used a liquid state machine and a
logistic regression classifier to construct a spiking neural network for
classifying 15 sitting postures. To allow this system to read our pressure data
into the spiking neurons, we designed an algorithm to encode map-like data into
cosine-rank sparsity data. The experimental results consisting of 15 sitting
postures from 19 participants show that the prediction precision of our SNN is
88.52%
Technical Evaluation of HoloLens for Multimedia: A First Look
A recently released cutting-edge AR device, Microsoft HoloLens, has attracted
considerable attention with its advanced capabilities. In this article, we
report the design and execution of a series of experiments to quantitatively
evaluate HoloLens' performance in head localization, real environment
reconstruction, spatial mapping, hologram visualization, and speech
recognition
An Overview of Serious Games
Serious games are growing rapidly as a gaming industry as well as a field of academic research. There are many surveys in the field of digital serious games; however, most surveys are specific to a particular area such as education or health. So far, there has been little work done to survey digital serious games in general, which is the main goal of this paper. Hence, we discuss relevant work on serious games in different application areas including education, well-being, advertisement, cultural heritage, interpersonal communication, and health care. We also propose a taxonomy for digital serious games, and we suggest a classification of reviewed serious games applications from the literature against the defined taxonomy. Finally, the paper provides guidelines, drawn from the literature, for the design and development of successful serious games, as well as discussing research perspectives in this domain
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