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The increasing need for large-scale deep neural networks (DNN) has made parallel training an area of intensive focus. One effective method, microbatch-based pipeline parallelism (notably GPipe), accelerates parallel training in various architectures. ...

The growing popularity of Chinese social media platforms such as Sina Weibo has created a large number of user generated text content, which is of great value for understanding public emotions. However, the existence of mixed languages in these ...

Facial expression recognition (FER) has wide applications in various domains such as healthcare, human-computer interaction, and more. However, the performance of existing FER algorithms is often compromised in low-light environments due to ...

Parallel training of large-scale networks has attracted the attention of both artificial intelligence and high-performance distributed systems. One of efficient parallelism is the micro-batch-based pipeline, e.g., GPipe. Based on the GPipe, we ...$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$

The patching and variable channel-independent mechanism has been used in the long-term multivariate time series (MTS) forecasting models to capture local semantic information and learn different attention patterns. However, it cannot exploit the ...

Existing facial expression recognition (FER) methods are mainly devoted to learning discriminative features from high-resolution images. However, when applied to low-resolution images, their performance drops rapidly. This paper proposes a ...

• Design a joint learning framework to alleviate the low-resolution FER challenge.
• Design novel attention-aware consistency and prediction consistency losses.
• Extensive experiments demonstrate the effectiveness of the proposed ...

Recently, facial expression recognition (FER) methods have achieved significant progress. However, FER is still challenged by factors such as uneven illumination and low-quality expression images. Exploring the potential of facial expression ...

Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines (VMs) after allocation is the traditional way for load balancing and consolidation. However,...

Recent studies have shown that deep learning has presented great potential in facial expression recognition (FER) tasks and attracted more and more researchers’ attention. Many existing methods have achieved good results on facial expression ...

Cloud computing, as a large-scale distributed computing system dynamically providing elastic services, is designed to meet the requirement of delivering computing services to users as subscription-oriented services. In general, the ...

• A new framework of local search algorithms and heuristic-based search routes.
• ...

Multi-Dimensional Resources Scheduling Problem (MDRSP, usually a multi-objective optimization problem) has attracted focus in the management of large-scale cloud computing systems as the collaborative operation of various devices in ...

• For multi-dimensional resource scheduling of Cloud, Growable GA is better than GA.

Cloud computing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical Cloud computing dividing the system into multi-levels with ...

• SFSSA to select algorithms
• HCCMS for faster management

Pattern counting is a crucial task in graph pattern mining. Accurate counting is not affordable as the datasets grow larger and larger, and approximate counting is getting popular to provide an estimated answer quickly. However, current ...

Automation applications have become one of the emerging trends in machine intelligence. In traditional automation applications, almost all operations are based on programming, scripts or Application Programming Interfaces. However, the continuous ...

Cloud computing has rapidly emerged as a model for delivering Internet-based utility computing services. Infrastructure as a Service (IaaS) is one of the most important and rapidly growing models in cloud computing. Scalability, ...

Recently, remarkable object detection and facial expression recognition (FER) approaches have been made by researchers. However, all of these models are trained and tested on high-resolution images without considering that low-...

Resource management in data centres continues to be a critical problem due to increased infrastructure complexity and dynamic workload conditions. Workload and energy consumption prediction are crucial for efficient resource management ...

• Workload forecasting and energy state estimation in cloud data centres.
• A model ...

Since its release, the Tensorflow framework has been widely used in various fields due to its advantages in deep learning. However, it is still at its early state. Its native distributed implementation has difficulty in expanding for large models ...

In autonomous driving and Intelligent transportation systems, pedestrian detection is vital in reducing traffic accidents. However, detecting small-scale and occluded pedestrians is challenging due to the ineffective utilization of the low-feature content ...

With the fast growth of big data applications, it has brought about a huge increase in the energy consumption for big data processing in Cloud data centers. In this study, a frequency-aware and energy-saving strategy based on dynamic voltage and ...