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"Optimising Automatic Text Classification Approach in Adaptive Online C" by Ya feng Zheng, Zhang hao Gao et al.

A text semantic classification is an essential approach to recognising the verbal intention of online learners, empowering reliable understanding and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as CNN and RNN, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to increase verbal recognition precision. The Bi-LSTM is considered to be an adaptive model to investigate semantic relations according to the context. Moreover, the attention mechanism in deep learning simulating human vision could assign weights to target texts effectively. This study proposed to construct a deep learning model combining Bi-LSTM and attention mechanism, in which Bi-LSTM obtained the v ....

Adaptation Models , Attention Mechanism , Deep Learning , Feature Extraction , Long Short Term Memory Network , Nline Collaborative Discussion , Task Analysis , Ext Categorization , Text Classification ,

"Context-Driven Satire Detection with Deep Learning" by Md Saifullah Razali, Alfian Abdul Halin et al.

This work discuss the task of automatically detecting satire instances in short articles. It is the study of extracting the most optimal features by using a deep learning architecture combined with carefully handcrafted contextual features. It is found that a few sets can perform well when they are used independently, but the others not so much. However, even the latter sets become very useful after the combination process with the former sets. This shows that each of the feature sets are significant. Finally, the combined feature sets undergoes the classification using well-known machine learning classification algorithms. The best algorithm for this task is found to be Logistic Regression. The outcome of all the experiments are good in all the metrics used. The result comparison to existing works in the same domain shows that the proposed method is slightly better with 0.94 in terms of F1-measure, while existing works managed to obtain 0.91 [1], 0.90 [2] and 0.88 [3]. The performance ....

Convolutional Neural Networks , Deep Learning , Feature Extraction , Machine Learning Algorithms , Natural Language Processing , Atire Detection , Support Vector Machines , Task Analysis ,

"SmartFilter: An Edge System for Real-Time Application-Guided Video Fra" by Jude Tchaye-Kondi, Yanlong Zhai et al.

Given the limited bandwidth available in distributed camera systems, it is nearly impossible for cameras to transmit their entire feed to the server in real-time. Furthermore, as the number of camera units increases, the processing overheads on the server also increase, resulting in excessive latencies. This paper introduces SmartFilter, a new Edge-to-Cloud filtering solution for video analytics. SmartFilter exploits the feedbacks from the running server-side application to filter directly on the camera, frames that are likely to produce the same application result as the previously offloaded ones. Because of its unique filtering mechanism, SmartFilter improves the system’s throughput, latency, network usage and reduces the server’s processing overhead while maintaining overall accuracy. SmartFilter is typically a fast and lightweight binary classifier that examines changes within frames to decide when these changes are significant enough to alter the application output. Experiment ....

Continuous Vision , Deep Learning , Edge Computing , Real Time System , Real Time Systems , Smart Cameras , Treaming Media , Task Analysis ,

"TreeNet Based Fast Task Decomposition for Resource-Constrained Edge In" by Dong Lu, Yanlong Zhai et al.

Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network’s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common featur ....

Adaptation Models , Computational Modeling , Deep Learning , Edge Computing , Edge Intelligence , Odel Acceleration , Model Compression , Neural Networks , Resource Constrained , Task Analysis ,

"A Novel Mix-Normalization Method for Generalizable Multi-Source Person" by Lei Qi, Lei Wang et al.

Person re-identification (Re-ID) has achieved great success in the supervised scenario. However, it is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. In this paper, we aim to tackle the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training) from the data augmentation perspective, thus we put forward a novel method, termed MixNorm. It consists of domain-aware mix-normalization (DMN) and domain-aware center regularization (DCR). Different from the conventional data augmentation, the proposed domain-aware mix-normalization enhances the diversity of features during training from the normalization perspective of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain. To further promote the eff ....

Adaptation Models , Data Models , Omain Aware Mix Normalization , Eneralizable Multi Source Person Re Identification , Task Analysis , Training Data ,