Zobrazeno 1 - 10
of 15 896
pro vyhledávání: '"Aliyu, IS"'
Today, children across Africa are at a growing risk from the Internet. Dangers include harmful content, violence, exploitation, abuse, and neglect. All these have increased due to increased mobile and Internet technology use, which not only places th
Externí odkaz:
http://arxiv.org/abs/2409.13159
Multimodal aspect-based sentiment analysis (MABSA) enhances sentiment detection by combining text with other data types like images. However, despite setting significant benchmarks, attention mechanisms exhibit limitations in efficiently modelling lo
Externí odkaz:
http://arxiv.org/abs/2408.15379
Aspect-based sentiment Analysis (ABSA) identifies and evaluates sentiments toward specific aspects of entities within text, providing detailed insights beyond overall sentiment. However, Attention mechanisms and neural network models struggle with sy
Externí odkaz:
http://arxiv.org/abs/2407.10347
Autor:
Kuwanto, Garry, Urua, Eno-Abasi E., Amuok, Priscilla Amondi, Muhammad, Shamsuddeen Hassan, Aremu, Anuoluwapo, Otiende, Verrah, Nanyanga, Loice Emma, Nyoike, Teresiah W., Akpan, Aniefon D., Udouboh, Nsima Ab, Archibong, Idongesit Udeme, Moses, Idara Effiong, Ige, Ifeoluwatayo A., Ajibade, Benjamin, Awokoya, Olumide Benjamin, Abdulmumin, Idris, Aliyu, Saminu Mohammad, Iro, Ruqayya Nasir, Ahmad, Ibrahim Said, Smith, Deontae, Michaels, Praise-EL, Adelani, David Ifeoluwa, Wijaya, Derry Tanti, Andy, Anietie
Publikováno v:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) 11349-11360
Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency an
Externí odkaz:
http://arxiv.org/abs/2407.10152
This paper addresses the challenge of representing complex human action (HA) in a nuclear power plant (NPP) digital twin (DT) and minimizing latency in partial computation offloading (PCO) in sixth-generation-enabled computing in the network (COIN) a
Externí odkaz:
http://arxiv.org/abs/2407.12011
Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multic
Externí odkaz:
http://arxiv.org/abs/2406.06595
The proliferation of online offensive language necessitates the development of effective detection mechanisms, especially in multilingual contexts. This study addresses the challenge by developing and introducing novel datasets for offensive language
Externí odkaz:
http://arxiv.org/abs/2406.02169
Aspect-Based Sentiment Analysis (ABSA) is increasingly crucial in Natural Language Processing (NLP) for applications such as customer feedback analysis and product recommendation systems. ABSA goes beyond traditional sentiment analysis by extracting
Externí odkaz:
http://arxiv.org/abs/2405.13013
With the emergence and proliferation of new forms of large-scale services such as smart homes, virtual reality/augmented reality, the increasingly complex networks are raising concerns about significant operational costs. As a result, the need for ne
Externí odkaz:
http://arxiv.org/abs/2405.08473
This paper addresses the problem of minimizing latency with partial computation offloading within Industrial Internet-of-Things (IoT) systems in in-network computing (COIN)-assisted Multiaccess Edge Computing (C-MEC) via ultra-reliable and low latenc
Externí odkaz:
http://arxiv.org/abs/2407.01540